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Neuroscience.

These windpipe cells trigger coughs to keep water out of the lungs

Neuroendocrine cells can sense substances on the way to the lungs and prompt reactions such as coughing and swallowing, experiments in mice show.

Tiny treadmills show how fruit flies walk

In ‘get the picture,’ science helps explore the meaning of art, more stories in neuroscience.

Chickadees use memory ‘bar codes’ to find their hidden food stashes

Unique subsets of neurons in a chickadee’s memory center light up for each distinct cache, hinting at how episodic memories are encoded in the brain.

Here’s how magnetic fields shape desert ants’ brains

Exposure to a tweaked magnetic field scrambled desert ants’ efforts to learn where home is — and affected neuron connections in a key part of the brain.

Dogs know words for their favorite toys

The brain activity of dogs that were expecting one toy but were shown another suggests canines create mental concepts of everyday objects.

Ancient viruses helped speedy nerves evolve

A retrovirus embedded in the DNA of some vertebrates helps turn on production of a protein needed to insulate nerve cells, aiding speedy thoughts.

How do babies learn words? An AI experiment may hold clues

Using relatively little data, audio and video taken from a baby’s perspective, an AI program learned the names of objects the baby encountered.

A new device let a man sense temperature with his prosthetic hand

A device that can be integrated into prosthetic hands capitalizes on phantom sensations to enable users to sense hot and cold.

Under very rare conditions, Alzheimer’s disease may be transmitted

Alzheimer’s isn’t contagious. But contaminated growth hormone injections caused early-onset Alzheimer’s in some recipients, a new study suggests.

Handwriting may boost brain connections more than typing does

Students asked to write words showed greater connectivity across the brain than when they typed them, suggesting writing may be a better boost for memory.

Electrical brain implants may help patients with severe brain injuries

After deep brain stimulation, five patients with severe brain injuries improved their scores on a test of cognitive function.

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Neuroscience

Human Brains May Be Getting Bigger

Brain size in one Massachusetts community has steadily increased since the 1930s, possibly explaining why dementia is trending lower nationwide

Hollywood Should Give Brain Science a Star Turn

Movies and TV shows frequently depict physical and biological sciences well, but often depict psychological and brain sciences poorly. Here’s why, and what we can do about it

Deena Weisberg, Marc Coutanche

Scientists Discover Extensive Brain-Wave Patterns

Certain brain layers specialize in particular waves—which might aid understanding of neuropsychiatric disorders

Simon Makin

Quiet! Our Loud World Is Making Us Sick

Experts describe ways to turn down the volume, from earbuds to smartphone apps that detect harmful noise levels

Joanne Silberner

How Air Pollution Damages the Brain

The new science of "exposomics" shows how air pollution contributes to Alzheimer’s, Parkinson’s, bipolar disorder and other brain diseases

Sherry Baker, OpenMind Magazine

A Random Influx of DNA from a Virus Helped Vertebrates Become So Stunningly Successful

Insertion of genetic material from a virus into the genome of a vertebrate ancestor enabled the lightning-quick electrical impulses that give animals with backbones their smarts

R. Douglas Fields

Think Seeing Is Believing? Think Again

We think that what we see represents stone-cold reality. Science has found out how wrong we can be.

Christie Aschwanden

How Animal Brains Tell Friends from Strangers

A small section of the mouse brain’s hippocampus uses specific neural codes to denote social familiarity and identity

Ingrid Wickelgren

Why Some Songs Make Everyone Want to Dance

A syncopated rhythm may prompt our brain to find the beat

Anna von Hopffgarten

Babies Smell Sweet, but Teenagers Stink. Is Evolution the Explanation?

Carboxylic acids make teens give off a pungent olfactory mix redolent of cheese, plum and even goats

Joanna Thompson

A ‘Havana Syndrome’ Investigation in Congress Rests on Politics, Not Science

Lawmakers should look in the mirror if they want answers to who hyped dubious reports of Havana syndrome. Instead they are investigating the spy agencies telling them the truth about the mystery

Robert Bartholomew

A Study in Primates Reveals How the Brain Encodes Complex Social Interactions

The research tracks, at the level of individual neurons, what happens when a monkey hangs out with other monkeys. It even found a possible neural code for empathy

Lydia Denworth

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Using Cognitive Neuroscience to Improve Mental Health Treatment: A Comprehensive Review

Jessica a. wojtalik.

Doctoral candidate at the University of Pittsburgh School of Social Work

Shaun M. Eack

Professor at the University of Pittsburgh School of Social Work and Department of Psychiatry

Matthew J. Smith

Associate professor at the University of Michigan School of Social Work

Matcheri S. Keshavan

Professor in the Harvard Medical School Department of Psychiatry

Mental health interventions do not yet offer complete, client-defined functional recovery, and novel directions in treatment research are needed to improve the efficacy of available interventions. One promising direction is the integration of social work and cognitive neuroscience methods, which provides new opportunities for clinical intervention research that will guide development of more effective mental health treatments that holistically attend to the biological, social, and environmental contributors to disability and recovery. This article reviews emerging trends in cognitive neuroscience and provides examples of how these advances can be used by social workers and allied professions to improve mental health treatment. We discuss neuroplasticity, which is the dynamic and malleable nature of the brain. We also review the use of risk and resiliency biomarkers and novel treatment targets based on neuroimaging findings to prevent disability, personalize treatment, and make interventions more targeted and effective. The potential of treatment research to contribute to neuroscience discoveries regarding brain change is considered from the experimental-medicine approach adopted by the National Institute of Mental Health. Finally, we provide resources and recommendations to facilitate the integration of cognitive neuroscience into mental health research in social work.

Mental health is an essential component of overall health and well-being ( World Health Organization, 2013 ), and mental illnesses—such as schizophrenia, depression, bipolar disorder, post-traumatic stress disorder (PTSD), and autism—are leading causes of disability in the U.S. ( U.S. Burden of Disease Collaborators, 2013 ). These mental health conditions are too often functionally disabling, impacting individuals’ ability to think clearly, live independently in the community, maintain meaningful interpersonal relationships, and achieve personal goals. Social workers are the largest group of mental health care providers in the U.S. ( Heisler & Bagalman, 2015 ) and have contributed to the development and testing of psychosocial interventions for people living with some of the most disabling mental health conditions (e.g., Anderson, Reiss, & Hogarty, 1986 ; Garland, 2013 ; Hogarty et al., 2004 ; Rapp, 1998 ; Stein & Test, 1980 ). Despite decades of focused research on pharmacological and psychosocial interventions to improve mental health—and despite numerous significant advances—the effectiveness of these interventions remains only moderately successful ( Bishop-Fitzpatrick, Minshew, & Eack, 2014 ; Mueser, Deavers, Penn, & Cassisi, 2013 ; Newby, McKinnon, Kuyken, Gilbody, & Dalgleish, 2015 ). Very few people with disabling mental illness achieve personally acceptable clinical and functional recovery, and even fewer return to levels of psychosocial functioning experienced prior to the onset of their condition ( Harvey & Bellack, 2009 ; Judd et al., 2008 ; McIntyre & O’Donovan, 2004 ). Recent advances in cognitive neuroscience research offer unique opportunities to improve the effectiveness of both pharmacological and psychosocial interventions for those confronted with mental health issues.

The integration of cognitive neuroscience and mental health research to increase the effectiveness of interventions that promote recovery from mental illness is a research priority ( National Institutes of Health, 2015 ), and social workers have a central role in such translational research efforts ( Brekke, Ell, & Palinkas, 2007 ). National Institute of Mental Health (NIMH) research priorities include investigating the neurobiological mechanisms and trajectories of mental health conditions to identify optimal therapeutic windows for intervention and possibly prevent illness onset. (For more information about the NIMH’s research priorities and associated funding announcements, see https://www.nimh.nih.gov/about/strategic-planning-reports/strategic-research-priorities/index.shtml .) In the current article, we review the emerging trends from cognitive neuroscience and brain plasticity research and provide examples of how these advances can be used by social workers and allied professions to improve mental health treatment (see Table 1 ). We encourage social work researchers, practitioners, and mental health educators to facilitate the integration of cognitive neuroscience by using this article to become familiar with the cognitive neuroscience vocabulary, introducing it into social work courses, and beginning to incorporate neuroimaging measures into their own research.

Key Points on Using Cognitive Neuroscience to Improve Mental Health Treatment

Note . NIMH 5 National Institute of Mental Health.

In 1990, U.S. President George H. W. Bush signed a proclamation designating 1990– 2000 as the “Decade of the Brain” ( Bush, 1990 ) to promote considerable neuroscience research efforts and raise public awareness of neurobiologically based conditions, such as schizophrenia and Alzheimer’s disease ( Goldstein, 1990 ). After nearly three decades, incredible progress has been made in the fundamental understanding of the human brain ( Insel & Landis, 2013 ; Jones & Mendell, 1999 ) and the neurobiological etiology of mental illness ( Charney, Buxbaum, Sklar, & Nestler, 2013 ; Goodkind et al., 2015 ). Indeed, many mental health conditions are now understood to involve numerous aspects of the brain and to follow a neurodevelopmental trajectory ( Ansorge, Hen, & Gingrich, 2007 ; Faludi & Mirnics, 2011 ; Keshavan, Anderson, & Pettergrew, 1994 ). Some of the most disabling mental health symptoms emerge in late adolescence and early adulthood ( Paus, Keshavan, & Giedd, 2008 ), which is a critical period of brain development ( Purves, White, & Riddle, 1996 ); the emergence of these symptoms has been shown to correspond with abnormal synaptic pruning and proliferation ( Faludi & Mirnics, 2011 ; Keshavan et al., 1994 ). This neurodevelopmental insult results in abnormal brain function ( Pantelis et al., 2003 ) and significant loss of gray matter volume ( Cannon et al., 2015 ), which underlie many of the signs and symptoms of mental illness (e.g., substantial cognitive challenges observed in individuals with schizophrenia; Fusar-Poli, Radua, McGuire, & Borgwardt, 2011 ; Rapoport, Giedd, & Gogtay, 2012 ).

Along with the evident neural basis and impact of many mental health conditions, the brain is also known to be “plastic.” Studies of neuroplasticity—the ability of the brain to adapt and change—suggest that the adult human brain has a remarkable capacity for strengthening and generating new neuronal connections to enhance daily functioning ( Bruel-Jungerman, Davis, & Laroche, 2007 ; Buonomano & Merzenich, 1998 ). Such findings have generated renewed therapeutic optimism for the possibility of greater recovery from mental health conditions that were previously thought to be characterized by static encephalopathy ( Goldberg, Hyde, Kleinman, & Weinberger, 1993 ). Thus, social workers have begun to integrate cognitive neuroscience methods into their research to understand and enhance the efficacy of their interventions (e.g., Eack et al., 2010 ; Garland, Froeliger, & Howard, 2015 ; Matto et al., 2013 ). For example, Eack, Newhill, and Keshavan (2016) demonstrated that individuals with schizophrenia who received a psychosocial treatment to improve their thinking skills had increased communication between frontal and temporal brain areas, which was associated with enhanced emotion processing. Such results suggest that frontotemporal neural communication may be an important treatment target for improving recovery. Moreover, one could conclude that cognitive training might be more effective if these interventions engaged clients in activities that would support enhanced communication between frontotemporal brain regions. Numerous social work investigators have written about the value of integrating cognitive neuroscience and clinical intervention research ( Farmer, 2008 ; Matto & Strolin-Goltzman, 2010 ; Matto, Strolin-Goltzman, & Ballan, 2014 ; Shapiro & Applegate, 2000 ).

Cognitive Neuroscience Basics

To understand the implications that cognitive neuroscience holds for informing mental health treatment, it is first essential to understand some basic knowledge about neuroscience and the brain. The field of cognitive neuroscience is directed toward the study of the neurobiological mechanisms that underlie mental processes and behaviors ( Frackowiak, 2004 ), and for the purposes of this article, we broadly include cognitive, affective, and clinical neuroscience domains under the cognitive neuroscience rubric. The brain is an incredible organ that can generally be defined as an integrated and complex information processing system that generates thoughts, emotions, and behaviors. The adult human brain is estimated to contain 100 billion neurons, which is comparable to the number of stars in the Milky Way galaxy ( Fischbach, 1992 ). Remarkably, all this processing power is packed into a mere 3 pounds of tissue (about 1300 cubic centimeters), and much of the brain’s function is being actively investigated and discovered.

Brain anatomy

The anatomy of the brain is broadly made up of gray matter, white matter, and cerebrospinal fluid. Gray matter comprises the cerebral cortex (i.e., the top and outer portions of the brain) and contains neuronal and glial cell bodies, dendrites, and unmyelinated axons (i.e., axons that do not have myelin containing sheaths). Unmyelinated axons are largely responsible for conscious and effortful mental processes ( Rosenzweig, Breedlove, & Watson, 2005 ), including memory and problem-solving. The cerebral cortex has a folded appearance, with the peaks known as gyri and the valleys referred to as sulci . White matter is located deeper in the brain and contains bundles of neuronal tracts that connect different areas of gray matter. These tracts encompass axons that are wrapped in myelin , which is an insulating fatty material that increases neuronal communication. White matter tracts interconnect and organize neuronal transactions in gray matter ( Rosenzweig et al., 2005 ). Among other functions, cerebrospinal fluid cushions the brain and fills the ventricles (i.e., fluid filled cavities) throughout the brain to protect it from injury during head movement.

The cerebral cortex is a primary focus of cognitive neuroscientists and is organized into five broad regions, or lobes—frontal, parietal, temporal, occipital, and limbic (see Figure 1 ). The frontal lobe is vital to a person’s ability to function within society, facilitating executive function (i.e., working memory), higher order social cognitive abilities (i.e., emotion regulation, theory of mind), self-awareness, moral reasoning, language, and voluntary movement ( Adolphs, 2001 ; Chayer & Freedman, 2001 ). The parietal lobe has a role in tactile and sensory processing. For example, spatial awareness, visuomotor actions (i.e., grasping an object), mathematical operations, imitation, and understanding intentions of others are cognitive abilities associated with the parietal lobe ( Dehaene, Piazza, Pinel, & Cohen, 2003 ; Fogassi & Luppino, 2005 ; Rizzolatti, Fogassi, & Gallese, 2001 ). The temporal lobe contains the primary auditory cortex involved in speech processing and the hippocampus involved in memory ( Squire & Zola-Morgan, 1991 ). Lower-order social cognition ( Adolphs, 2001 ; LaBar, Crupain, Voyvodic, & McCarthy, 2003 )—such as perceiving socially relevant information and recognizing emotions in faces—is also associated with the temporal lobe. The occipital lobe is the location of the primary visual cortex and is involved in visual processing. Finally, the limbic system and insular cortices ( Figure 1 ) are evolutionally older regions located deep in the brain and are central to basic human functioning. Physical and social pain are processed in the insula ( Craig & Craig, 2009 ; Kross, Berman, Mischel, Smith, & Wager, 2011 ), and impulsivity, reward, and emotions are associated with the limbic system ( McClure, Laibson, Loewenstein, & Cohen, 2004 ; Morgane, Galler, & Mokler, 2005 ). A basic knowledge of the brain’s functional anatomy is important for understanding how various neuroimaging methods assess brain structure and function. It is also important to remember that the brain is a vast and highly connected network, and that cognitive and behavioral functions are largely the result of the coordination of numerous brain regions that can span lobes.

Visualization of the broad regional organization of the human cerebral cortex.

Neuroimaging approaches

Neuroimaging methodologies are noninvasive techniques used in cognitive neuroscience to assess the structure and function of the human brain. Magnetic resonance imaging (MRI) is a technique for assessing brain structure that produces a high-spatial-resolution image of brain anatomy, including gray matter, white matter, and cerebrospinal fluid. MRI is one of the most commonly used neuroimaging techniques for assessing neuroanatomy. Common measures of brain function (or activity) include positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). Both measure brain activation over a period of time by indirectly assessing blood flow while a participant is performing a cognitive task in the scanner. More specifically, fMRI measures the blood oxygenation level dependent (BOLD) signal and reflects changes in blood flow ( hemodynamic response ) that correspond to neural activity ( Huettel, Song, & McCarthy, 2008 ). PET scanning requires the injection of a radioactive tracer, which is then measured (as opposed to intrinsic blow flow) to obtain an assessment of brain function ( Huettel et al., 2008 ); PET is used less frequently due to the need to inject the radioactive tracer. Other neuroimaging techniques index neural communication patterns and can reflect either functional or structural connectivity throughout the brain ( Friston, 1994 , 2011 ; Hermundstad et al., 2013 ). For example, resting-state fMRI assesses functionally linked brain areas while the person is resting (i.e., not performing a task in the scanner; Van Den Heuvel & Pol, 2010 ). Similarly, task-based functional imaging studies measure correlated patterns of brain activity when a person is performing a task in the scanner ( Barch et al., 2013 ). In contrast, diffusion tensor imaging (DTI) is used to examine structural connectivity, which is a measure of how well white matter tracts connect with gray matter ( Sporns, Tononi, & Kötter, 2005 ). Together, these different neuroimaging methodologies provide powerful strategies for understanding how the brain is organized, how it functions when processing information, and how various regions communicate to support cognition and behavior. Perhaps most importantly, all of these measured parameters can be modified by mental health interventions ( Barsaglini, Sartori, Benetti, Pettersson-Yeo, & Mechelli, 2014 ; Messina, Sambin, Palmieri, & Viviani, 2013 ; Ramsay & MacDonald, 2015 ).

The Dynamic, Neuroplastic Brain

As neuroimaging methods rapidly advance, data increasingly indicate that the brain is malleable, neuroplastic, and reciprocally influenced by the social environment, leading to entirely new opportunities for social work interventions to enhance psychosocial functioning and recovery. For many years, neuroscientists steadfastly believed that once the brain reached its mature state in adulthood, the number of neural connections were fixed. However, early cognitive neuroscience research in rodents demonstrated that environmental enrichment increased neuronal connections, providing the first evidence of neuroplasticity ( Diamond, Krech, & Rosenzweig, 1964 ). Neuroplasticity is the capability of the brain to be flexible, dynamic, and adjust to the environment by creating, rebuilding, or strengthening neuronal connections, especially in response to learning or injury ( Bruel-Jungerman et al., 2007 ; Buonomano & Merzenich, 1998 ). In the 1980s, Taub (1980) advanced the understanding of neuroplasticity ( Schwartz & Begley, 2003 ) by revealing that neural connections could be regenerated in the somatosensory cortex of monkeys after the connections controlling one arm were severed. Taub and colleagues (1993) later applied these findings to rehabilitating neural connections important for daily living skills after a stroke using constraint-induced movement therapy. This therapy forces the reorganization of neural connections—often motor neurons—in the brain area damaged by stroke. Individuals treated with constraint-induced movement therapy practice movements using the impacted limb by constraining the unimpacted, contralateral appendage ( Taub, Uswatte, & Mark, 2014 ). Taub’s original findings provided impetus to study adult brain plasticity, and the last 40 years of research in this area has demonstrated convincing evidence that the adult brain is, in fact, capable of remarkable changes through the creation and adaptation of neural connections based on environmental experience ( Fuchs & Flügge, 2014 ).

People exist in an ever-changing physical and social environment that influences brain development and function, which has implications for mental health treatment research. Given this plasticity feature, the brain is always changing. For instance, individuals who develop a mental illness often significantly withdraw to an impoverished and isolated social life ( Hooley, 2010 ). Thus, exposure to social deprivation, neglect, and stress negatively influences the brain by modifying circuits (i.e., preventing the creation of new neuronal connections) responsible for cognitive function ( Lu et al., 2003 ). For example, experiencing chronic stress can be neurotoxic and inhibit factors important for neuronal health ( Pittenger & Duman, 2008 ). This effect of stress contributes to abnormal brain functioning and gray matter loss, and it has been observed to lead to cognitive impairment in people with depression ( Fossati, Radtchenko, & Boyer, 2004 ), schizophrenia ( Cannon et al., 2015 ), and autism ( Berger, Rohn, & Oxford, 2013 ). Within this special section on social work and neuroscience, the article “The Neuroscience of Resilience” by Hunter, Gray, and McEwan (in press) discusses the effects of early adversity and stress on the brain. As Hunter et al. note, it is increasingly clear that negative social environments and experiences induce adverse neuroplasticity, changing the brain for the worse (i.e., gray matter loss). These findings are particularly relevant for social work because they provide evidence of the powerful role that the social environment plays in brain development and function—an important assumption of the biopsychosocial model and prevention efforts.

Fortunately, supportive and enriched social environments can facilitate adaptive brain plasticity ( Davidson & McEwen, 2012 ; Keshavan, Mehta, Padmanabhan, & Shah, 2015 ) by strengthening or generating novel neural connections reflected in increased gray matter and/or brain activation. Research in healthy volunteers has provided some of the first evidence of adaptive neuroplasticity in adulthood through learning or training ( Driemeyer, Boyke, Gaser, Büchel, & May, 2008 ; Maguire et al., 2000 ). For example, participants who learned to juggle over a 3-month period displayed increased gray matter in the middle temporal gyrus and intra-parietal sulcus—which are involved in visuomotor and visuospatial processing— compared to a nonjuggling group ( Draganski et al., 2004 ). Regarding mental health, the principles of neuroplasticity are the driving force for cognitive remediation interventions ( Keshavan, Vinogradov, Rumsey, Sherrill, & Wagner, 2014 ; Morimoto, Wexler, & Alexopoulos, 2012 ), such as cognitive enhancement therapy (CET) for schizophrenia ( Hogarty et al., 2004 ) and autism ( Eack et al., 2013 ), and neuroplasticity-based computerized cognitive remediation for older adults with depression ( Morimoto et al., 2012 ). Participation in such interventions is thought to capitalize on neurobiological plasticity reserves to restore or enhance neural connections through repeated practice of cognitive exercises in an enriched social environment ( Keshavan & Hogarty, 1999 ; Morimoto et al., 2014 ). Unfortunately, outside of cognitive remediation, few discoveries surrounding brain plasticity have been translated to the development or refinement of other clinical interventions for mental illness ( Cramer et al., 2011 ), although notable examples are emerging ( Matto et al., 2013 ).

Overall, the neuroplasticity literature validates the long-held biopsychosocial perspective of social workers that socioenvironmental and genetic factors interact to influence neurobiology ( Garland & Howard, 2009 ). In the context of mental health treatment research, the principles of neuroplasticity are also reminiscent of the foundational framework of person-in-environment ( Bartlett, 1970 ) that postulates a bidirectional relationship between neurobiology and environment ( Green & McDermott, 2010 ). The dynamic, neuroplastic nature of the brain affords many opportunities for social workers to use cognitive neuroscience approaches in their translational research, thereby facilitating the development of more targeted and effective interventions that could directly address core pathophysiological processes to promote mental health recovery.

How Can Cognitive Neuroscience Research Improve Mental Health Treatment?

Early detection of people at risk for mental illness.

The current system for diagnosing mental health conditions is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association, 2013 ). In this system, a diagnosis is made after the onset of mental illness based on presenting behavioral symptoms that meet a set of criteria. However, neural signs of mental health symptoms are often present years before the onset of the condition, opening the window for very early intervention or possible prevention. These neural signs are known as biomarkers —biological indicators of a medical condition or disease state. In the context of cognitive neuroscience research, a biomarker is a measurable neurobiological feature indicating that a person is at risk of developing certain mental health symptoms ( Singh & Rose, 2009 ). Abnormal patterns in brain activation while performing a task and/or reduced gray matter volume in regions supporting cognitive functioning are simple but frequently reported examples of neural biomarkers that may signal mental illness vulnerability ( Chang et al., 2017 ; Wackerhagen et al., 2017 ; Walter et al., 2016 ). Biomarkers are an ongoing area of investigation, and a valid and reliable marker for diagnosis does not yet exist for any major mental health condition. However, the use of neuroimaging-based biomarkers has the exciting potential to make mental health screening and diagnostic models more accurate, thereby increasing scientific opportunities for applied clinical research in very early intervention and preventive treatments.

Multivariate statistics—methods for analyzing multiple dependent variables at once (i.e., partial least squares)—are a common and robust approach to mental health biomarker research ( Haxby, Connolly, & Guntupalli, 2014 ). In the neuroscience literature, this statistical approach is generally known as multivariate pattern analysis or multivariate pattern recognition. Simply put, multivariate pattern analysis uses data on gray matter volume from MRI or brain activity from fMRI (the dependent variables) to classify people with and without a mental illness with high sensitivity and specificity ( Borgwardt & Fusar-Poli, 2012 ; Krishnan, Williams, McIntosh, & Abdi, 2011 ). Sensitivity indicates the ability of multivariate pattern analysis to correctly identify the proportion of individuals with a mental illness in a given sample, and specificity is the ability to correctly identify the proportion of individuals without a mental illness. For example, two recent meta-analyses were conducted across all neuroimaging studies using multivariate pattern recognition in major depression ( Kambeitz et al., 2016 ) and schizophrenia ( Kambeitz et al., 2015 ). Across 33 included neuroimaging studies, differential patterns in brain structure and activity correctly classified individuals with major depression and healthy volunteers at 77% sensitivity and 78% specificity. The meta-analytic findings among 38 neuroimaging studies in schizophrenia were similar, with high sensitivity (80%) and specificity (80%) in correctly classifying individuals with schizophrenia and healthy volunteers based on patterns in brain structure and function ( Kambeitz et al., 2015 ). Multivariate pattern analysis has also demonstrated capabilities in deciphering between different mental health diagnoses. Using resting-state fMRI data, multivariate pattern analysis correctly classified individuals with schizophrenia at 92% sensitivity and individuals with bipolar disorder at 83% sensitivity ( Calhoun, Maciejewski, Pearlson, & Kiehl, 2008 ). These findings have important implications for the use of neuroimaging biomarkers in diagnostic models that can accurately detect individuals very early in the course of illness to avoid long durations of untreated illness, inappropriate treatments, and disability.

From a more preventive perspective, multivariate pattern analysis has shown success in classifying individuals at ultra-high risk of developing psychosis. With a 92% sensitivity rate, structural MRI patterns differentiated individuals who developed psychosis from healthy volunteers ( Koutsouleris et al., 2012 ). The classification accuracy rate for the ultra-high risk individuals who did not transition to psychosis from those who did was 84% based on structural brain patterns. In trauma-related conditions, gray matter pattern differences were used to accurately classify 67% of trauma survivors who developed PTSD compared to trauma survivors who did not develop PTSD ( Gong et al., 2013 ). Such results have exciting implications for identifying and personalizing trauma-related interventions based on biomarkers that can predict those at risk of developing PTSD after a traumatic experience. Using diagnostic models from structural and functional brain scans has significant clinical utility for identifying phenotypes of mental illness risk and recovery. As such, the use of cognitive neuroscience research to detect neuroimaging-based biomarkers that are predictive of several forms of mental illness can improve mental health treatment by moving it from a tertiary form of prevention to a practice of primary prevention.

It should be noted, however, that there are several ethical concerns in neuroimaging-based biomarker research that are worthy of discussion ( Singh & Rose, 2009 ). First and foremost, before biomarker research can be translated to clinical practice, there must be a careful, cautious approach to developing therapeutic practice methods for when and how to inform people about their risks and/or resiliencies to mental illness based on neurobiology. This leads to the second ethical dilemma of biomarker research: that identifying a client at risk for mental illness can be potentially traumatizing and stigmatizing, especially considering that multivariate pattern analysis does not yet have 100% classification sensitivity. Third, biomarkers have only been studied in the context of research labs, making generalizability to clinical practice low ( Cook, 2008 ). Lastly, the use of biomarkers is costly, as MRI scanners are expensive and may not be accessible in rural communities. Trained experts are also required to operate the scanner, analyze the data, and interpret the findings ( Lakhan, Vieira, & Hamlat, 2010 ). Despite these considerations and the need for increased accuracy in multivariate models, cognitive neuroscience research aimed at identifying biomarkers of mental illness holds promise for improving mental health treatment by directing interventions to those who are most vulnerable. Pattern-recognition methods show particular promise for paving the way for effective early intervention strategies to prevent disability ( Woo, Chang, Lindquist, & Wager, 2017 ).

Identifying those who are resilient to mental illness

Neuroimaging-based biomarker research is also a rich area for identifying the brain signatures of resiliency. By understanding who is exposed to an adverse social environment and does not develop a mental health condition, we can learn about how the brain can protect itself. The study of the neural substrates that protect people from developing a mental health condition is a new area of investigation. However, themes are emerging suggesting that better regulated and organized neural networks for reward processing, emotion regulation, and stress management may be features of sustained mental health, even in the context of significant stress and trauma ( van der Werff, van den Berg, Pannekoek, Elzinga, & Van Der Wee, 2013 ). For example, a recent study by Swartz, Knodt, Radtke, and Hariri (2015) demonstrated that lower amygdala activation (implicated in emotion regulation) while viewing angry and fearful faces during a baseline scan in 340 healthy young adults predicted lower psychiatric vulnerability to life stressors up to 4 years later.

Neurobiological differences between people who develop or do not develop post-traumatic sequelae—such as PTSD ( Horn, Charney, & Feder, 2016 ) and major depression ( Han & Nestler, 2017 )—have provided additional clues for resiliency biomarkers of mental illness. Lower gray matter or reduced fear-related activation in the amygdala is a common protective biomarker of PTSD, as it may signal healthier regulation of emotional responses to stressful experiences ( Gupta et al., 2017 ; Kuo, Kaloupek, & Woodward, 2012 ; Morey, Haswell, Hooper, & De Bellis, 2016 ). Greater gray matter volume in the hippocampus and dorsolateral prefrontal cortex appear to be features of resiliency against developing major depression, especially for individuals with a family history of the condition ( Amico et al., 2011 ; Arnone, McIntosh, Ebmeier, Munafò, & Anderson, 2012 ; Rao et al., 2010 ). Explicit memory, which is the conscious retrieval of a thought, relies on communication (connectivity) between the dorsolateral prefrontal cortex and hippocampus and is a prominent cognitive challenge for people with major depression ( Pittenger & Duman, 2008 ). Therefore, having more and stronger neural connections from the dorsolateral prefrontal cortex to the hippocampus (e.g., larger gray matter volumes) underpinning effective explicit memory may be a neuroprotective factor against depression.

The neuro-resiliency field is growing, and common themes across mental health conditions have started to emerge. The amygdala has been prominently implicated in many studies, and this small limbic lobe brain structure is thought to be responsible, in part, for the generation of fear and other emotions ( Costafreda, Brammer, David, & Fu, 2008 ). A frequent finding is that an overactive amygdala places a person at greater risk for mental health challenges ( McLaughlin et al., 2014 ; Olsavsky et al., 2012 ; Zhong et al., 2011 ). In contrast, those whose amygdala appears to be under better control during stress are often less likely to experience trauma and other mental health-related symptoms ( Swartz et al., 2015 ). At the same time, increased integrity of prefrontal structures, which are involved in regulating amygdalar activity, also portend increased resiliency, even in the face of significant social and environmental adversity that would often contribute to the onset of a mental health condition ( Herringa et al., 2016 ; Tottenham & Galván, 2016 ). Such findings highlight the complex interplay between brain networks in supporting healthy psychological adjustment and begin to indicate the optimal states of the brain that can support resiliency and adjustment during the experience of life stressors.

Having a greater number of whole-brain neuronal connections (known as “brain reserve”) is another promising neural marker for resiliency ( Satz, 1993 ; Stern, 2002 ). Historically, the protective effects of brain reserve were discovered in postmortem brain studies of Alzheimer’s disease. Neuroscientists observed that, despite the presence of neuropathic plaques and tangles of Alzheimer’s disease in the postmortem brains, certain individuals did not experience the cognitive signs or symptoms of the disease as they aged ( Katzman et al., 1988 ). Such findings indicate that pathophysiology can exist in the brain, but protective mechanisms such as brain reserve can intervene in the clinical manifestation of the condition ( Mortimer, 1997 ). According to Mortimer (1997) , the brain reserve mechanism of action is thought to be a redundancy or larger number of neural connections that are readily available to compensate for the occurrence of lesions or disease insult to the brain ( Keshavan et al., 2011 ).

To summarize, neuroimaging-based biomarkers of resiliency have obvious clinical utility for screening, prevention, and personalization of mental health treatment through an understanding of how the brain can protect itself. Neuro-resiliency research will aid in the identification of which neural circuits must be strengthened to prevent mental illness and promote healthy psychosocial functioning and adjustment. In the social work context, neuro-resiliency research also raises new person-in-environment questions around why some people do not acquire neurodevelopmental impairments or harm in the face of extreme stress. For example, given the malleable nature of the brain, do resilient individuals who experience adversity, oppression, or discrimination have a more neuroplastic brain that is capable of rapidly adapting to change in the social environment, compared to less resilient people? How can psychosocial interventions facilitate a neurobiological foundation that is resilient to stress in some of the most underprivileged and oppressed populations?

Identifying neural treatment targets for mental health interventions

Cognitive neuroscience has perhaps gained the most traction in promoting mental health treatment through the identification of neural targets for intervention. By understanding the neurobiological mechanisms of mental illness, cognitive neuroscience research has generated a significant amount of evidence indicating where to target medications, brain stimulation, and psychosocial interventions. By providing in vivo measurements of brain function and structure during mental health treatment, cognitive neuroscience provides a window to observe the relationship between treatment parameters and distinct clinical and functional improvements. Such research will increase opportunities to make mental health treatments more targeted and precise. For example, suppose a new cognitive training intervention was observed to increase prefrontal brain activity associated with improved attention and ability to maintain employment. This observation would indicate that it may be optimal to emphasize certain training exercises associated with prefrontal cognitive control and attention for functional recovery in the employment domain. Intervention developers can then use this information to modify their cognitive training program to focus on the attention-training elements to provide a more targeted, less lengthy treatment experience that could be increasingly efficacious for improving vocational functioning. This target-engagement approach is an essential component of experimental medicine (see Insel, 2012 , p. 24), an approach to developing mental health interventions adopted by NIMH ( Insel, 2015 ). Clearly, such knowledge would help intervention research to prioritize novel and refined treatment strategies to improve mental health interventions.

Cognition, daily functioning, and motivation are all behavioral domains that are frequently impacted by mental health conditions. The identification and understanding of the neural underpinnings driving these behavioral domains can serve as mental health treatment targets. For example, in people with schizophrenia, lower gray matter volume in frontal, temporal, and limbic regions is associated with challenges in social and nonsocial cognition ( Wolf, Höse, Frasch, Walter, & Vasic, 2008 ; Yamada et al., 2007 ). Meta-analytic evidence has similarly demonstrated that lower gray matter volume in frontal and limbic regions is a neural correlate of functional abilities in people with schizophrenia ( Wojtalik, Smith, Keshavan, & Eack, 2017 ). More specifically, better social functioning in people with schizophrenia is predicted by greater empathy-related activation in the middle cingulate cortex of the limbic lobe ( Smith et al., 2015 ) and increased connectivity between the medial prefrontal cortex and posterior cingulate cortex ( Fox et al., 2017 ). In people with schizophrenia, there is substantial regional overlap among the frontal, limbic, and temporal lobes, which support cognitive and functional outcomes. These brain regions may be important neurobiological substrates that could be targeted through pharmacological and/or psychosocial interventions to promote a more complete recovery from schizophrenia.

Regarding motivation, the striatum is an important limbic lobe region affecting motivational outcomes across people with schizophrenia, major depression, and bipolar disorder ( Whitton, Treadway, & Pizzagalli, 2015 ). This transdiagnostic neural target is associated with underactivation in people with major depression and schizophrenia, which translates to a lack of motivation (i.e., negative symptoms, anhedonia). In bipolar disorder, hyperactivation in the striatum is linked to oversensitivity to reward and high motivation for rewarding experiences (i.e., mania; Whitton et al., 2015 ). Compared to healthy volunteers, individuals with bipolar disorder recruit significantly more striatal activity during the anticipation of monetary rewards, possibly representing a neural determinant of elevated mood ( Nusslock et al., 2012 ). Others have observed lower striatal activation during reward processing in major depression and schizophrenia. For example, Subramaniam et al. (2015) observed significantly less activation in the striatum during monetary reward anticipation in people with schizophrenia when compared to healthy individuals. In healthy volunteers, greater activation in the striatum was associated with an increased sense of pleasure and motivation after receiving a monetary reward, but similar activation was not observed in individuals with schizophrenia.

For interventionists, these results suggest that increasing recruitment of the striatum during anticipatory reward processing in clients with major depression and schizophrenia (and decreasing striatal activity in bipolar disorder) is a potentially promising treatment target to improve motivational difficulties. For example, behavioral treatments that encourage increased motivation by positively reinforcing rewarding behaviors ( Favrod, Giuliani, Ernst, & Bonsack, 2010 ) such as exercising ( Dauwan, Begemann, Heringa, & Sommer, 2016 ; Firth, Cotter, Elliott, French, & Yung, 2015 ) appear to activate the striatum ( Robertson et al., 2016 ). Repeated reinforcement of a rewarding behavior may alter the striatum so that over time it will become more intrinsically activated by the anticipation of reward, which may generalize to improved motivational outcomes in clients outside of the clinic. In this way, knowledge of the neural contributors to different domains of behavioral outcomes in people with mental health conditions can improve the precision of mental health treatments by providing direct neurobiological targets for intervention. Based on growing data on the plasticity of the brain and its impact by the social environment, it is increasingly plausible to address these neural targets with non-pharmacological interventions ( Keshavan et al., 2014 ).

Using cognitive neuroscience research to personalize mental health treatment

An emerging theme in the earlier sections of this paper is the strong potential for biomarkers and treatment targets to help make mental health treatment more personalized. Because mental illness has a substantial brain component, and even single conditions are highly heterogeneous, understanding a person’s neurobiology will help select the treatment protocols most likely to work the first time, or most quickly, rather than having clients endure repeated treatment failures. The complex interplay between genetics, environment, and neurobiology—along with the uniqueness of each person—makes true personalization of mental health treatment quite challenging. However, neuroimaging-based biomarkers provide information about neurobiological features that could accurately determine which pharmacological or psychosocial treatments would be the best fit for specific clients, at least from a neurobiological perspective ( Holsboer, 2008 ). The NIMH promotes the utility of cognitive neuroscience tools for identifying neural substrates that can be used to personalize mental health treatment ( Cuthbert, 2014 ). The Research Domain Criteria (RDoC) is an NIMH initiative that encourages a dimensional approach to understanding mental illness as an alternative to the current categorical diagnostic system ( Insel et al., 2010 ; NIMH, n.d. ). The strategic goal of RDoC is to fund research that will identify the neural basis of differential dimensions of observable behavior and functioning in order to inform treatment personalization based on neurobiological features ( Morris & Cuthbert, 2012 ).

The use of cognitive neuroscience to predict responsiveness to treatment has been a particularly important avenue for determining the neural mechanisms of personalizing mental health treatment. For example, an early study by Bryant et al. (2008) observed that optimal response to cognitive–behavioral therapy (CBT) 6 months after completion was related to lower fear-related activity in the amygdala and anterior cingulate cortex of the limbic lobe in clients with PTSD. Lower amygdala reactivity to fearful faces is likely reflective of a lower fear response ( Bryant et al., 2008 ). A more recent study in PTSD found that having larger hippocampal volume at the start of prolonged exposure was predictive of better treatment response ( Rubin et al., 2016 ). The capacity to extinguish traumatic memories may be a capability associated with larger hippocampal volume ( Rubin et al., 2016 ). In the context of treatment personalization, it can be inferred that the absence of these neurobiological features (i.e., lower amygdala reactivity and larger hippocampus) in clients with PTSD is rate-limiting. These clients may require emotion regulation and memory training prior to the start of CBT or prolonged exposure to optimize response.

In the case of early course schizophrenia, having a greater amount of cortical gray matter at the start of cognitive remediation is associated with an accelerated improvement in social cognition. People with lower cortical gray matter benefit from treatment, but the rate of social cognitive improvement is slower ( Keshavan et al., 2011 ). Such results indicate that cortical gray matter volume can be assessed at the start of cognitive remediation to personalize the length of treatment and which cognitive domains to target for optimizing recovery. Finally, Dunlop et al. (2017) used multivariate pattern analysis to examine connectivity signatures of clients with major depression who responded to CBT and antidepressant medication compared to clients who were not responsive to treatment. Differential resting-state connectivity patterns in the cingulate cortex accurately classified clients who failed to respond to treatment against clients who did respond at an average of 82%. These results again demonstrate the clinical utility of using cognitive neuroscience to aid in the identification of effective first-line treatment. This is particularly important given that treatment failures and the revolving-door experience can be demoralizing for clients and a significant barrier to treatment engagement ( Andrade et al., 2014 ).

How Can Mental Health Treatment Identify Brain Mechanisms Underlying Cognition, Affect, and Behavior?

Although much can be learned about improving mental health treatment by incorporating data and discoveries from cognitive neuroscience, intervention research also provides a powerful platform for understanding the brain and how it changes when exposed to treatment (see Table 1 ). The longitudinal context of treatment research, as well as the experimental manipulation of interventions, goes far beyond the cross-sectional studies that characterize much of cognitive neuroscience. Clinical trials afford unique opportunities for understanding the plasticity of specific brain regions, how the brain changes in response to psychosocial intervention, and the connection between brain changes and meaningful treatment outcomes. The capacity for intervention research to yield significant findings for cognitive neuroscience has been particularly represented by the experimental-medicine approach adopted by NIMH ( Insel, 2015 ).

Experimental medicine

Experimental medicine is a novel approach adopted by NIMH to, in part, integrate biological and treatment research, and to use the experimental and longitudinal context of clinical trials to yield new cognitive neuroscience discoveries in a more robust context than cross-sectional research. The basic premise of the approach is that interventions to improve mental health usually have underlying targets that mediate treatment outcomes. More specifically, this approach uses neural markers and their change by treatments as proxy outcome measures in clinical trials, thereby making treatment research more efficient and leading to quicker answers ( Lewandowski, Ongur, & Keshavan, 2018 ). These targets often implicate cognitive neuroscience constructs, such as increased brain communication and better outcomes in autism ( Plitt, Barnes, Wallace, Kenworthy, & Martin, 2015 ) or lower prefrontal activity predicting social disability in schizophrenia ( Wojtalik et al., 2017 ). The experimental-medicine approach makes these intervention targets explicit foci of treatment. The experimental context of clinical trials is used to intervene on these targets and assess the impact on meaningful mental health outcomes ( Insel, 2012 ). It is hypothesized that two important advances will result from this approach. First, mental health treatment research will accelerate by focusing on target engagement and a “fast-fail” approach to intervention development. In such an approach, researchers perform smaller and more rapid clinical trials to identify treatment targets that show malleability and connection to favorable outcomes and quickly abandon those targets that do not ( Insel & Gogtay, 2014 ). Second, the underlying mechanisms contributing to mental illness will be discovered as researchers identify which targets contribute to mental health outcomes. As such, experimental medicine views treatment research not only as a tool for building better interventions, but also for discovering the targets—often neurobiological in nature—that do and do not contribute to mental health ( Insel, 2012 , 2015 ).

The experimental-medicine paradigm comes from the broader biomedical treatment literature for physical illnesses, including oncology. Experimental medicine is an attempt by NIMH and the scientific community to align mental health intervention research with medical research in other fields. Major advances in cancer research have been accomplished by increasing the focus on potential targets for tumor development and proliferation, ruling out those targets that show no signs of contributing to pathophysiology and noting targets that are less tractable to intervention ( Jones & Price, 2012 ). Many of the most significant treatment advances in mental health have serendipitously taken this route, such as when dopamine antagonists were discovered to treat schizophrenia and serotonin reuptake inhibitors were identified for the treatment of depression. The neural contributors to these conditions were largely formulated around observations that such medications affected a neural target, and when that neural target changed, mental health symptoms improved substantially. This is why schizophrenia is known to involve dopaminergic hyperactivity and why depression is known to involve serotonin neurotransmission. However, not all mental health issues are amenable to such an approach ( Markowitz, 2016 ). Experimental medicine is an attempt to learn more from treatment research—not only about what works, but about the mechanisms that contribute to and ultimately cause mental illness. As such, treatment research can significantly inform research on the cognitive neuroscience of mental health, particularly in understanding where and how the brain can change, as well as the changes that are most needed to support improved recovery and psychosocial functioning.

Identifying where the brain changes in response to mental health treatment

The discovery of brain plasticity and the significant interplay between the social environment and neural change has ushered in a new era of understanding the capabilities of human neurobiology. It is now widely recognized that the brain is malleable and continuously shaped beyond early development by learning, interaction, and the environment ( Bruel-Jungerman et al., 2007 ; Buonomano & Merzenich, 1998 ). What is less known is where the brain can change, and treatment research is well poised to contribute knowledge to this area. Like many phenomena, brain plasticity is expected to be unevenly distributed ( Buonomano & Merzenich, 1998 ). Perhaps the most “neuroplastic” area discovered to date is the hippocampus, a region in the middle of the brain that is heavily involved in memory storage. This finding is largely drawn from experimental animal studies ( Soya et al., 2007 ; Van Praag, Shubert, Zhao, & Gage, 2005 ), and human exercise-physiology studies ( Erickson et al., 2011 ; Fuss et al., 2014 ). Researchers have conducted numerous studies on the impact of aerobic activity on hippocampal volume and memory across many different population groups. For example, Erickson and colleagues (2011) completed a randomized trial of a 40-minute walking intervention versus a stretching control in older adults and found that hippocampal volume was significantly increased in the moderate exercise condition relative to control. Furthermore, these volumetric changes were associated with significant improvements in memory, perhaps reversing age-related memory decline by several years ( Erickson et al., 2011 ).

For some time it was thought that only the hippocampus might possess this remarkable ability to self-repair and generate new neurons ( Deng, Aimone, & Gage, 2010 ), but additional studies are emerging indicating that plasticity is possible in other areas of the brain to support improved function in different domains. For example, Wang and colleagues (2016) examined the effects of CBT on brain communication patterns in adults with attention deficit hyperactivity disorder and found significant increases in the communication between frontal and parietal regions among participants treated with CBT. These neural changes were associated with reduced symptoms of attention deficit hyperactivity disorder ( Wang et al., 2016 ). Another study of CBT by Shou and colleagues (2017) found increased frontoparietal connectivity with the amygdala in adults with stress-related conditions who were receiving treatment, again suggesting that CBT can impact neural communication patterns across diverse areas of the brain in support of improved mental health outcomes ( Shou et al., 2017 ). A recent meta-analytic review of the impact of cognitive remediation interventions on brain function in schizophrenia found widespread increases in brain activity throughout the prefrontal cortex associated with treatment ( Ramsay & MacDonald, 2015 ). Although these intervention studies focus on brain function and communication, and it remains unclear the degree to which the generation of new neurons outside of the hippocampus is possible, it is increasing evident that many brain regions have greater functional plasticity than previously recognized.

Much of what is known about the capacity of different regions of the human brain to change has been generated from mental health intervention research. Whether it is aerobic interventions to protect against memory loss or cognitive exercises to enhance attention and problem-solving, these studies are a driving force behind what is known about where the brain can change. The longitudinal and often experimental nature of mental health treatment research provides a powerful platform for probing and understanding brain plasticity. Although a great deal remains to be learned and the distribution of functional and structural plasticity throughout the human brain is only beginning to be mapped, there is considerable optimism that much of our neural make-up could be amenable to positive change and influence ( Cramer et al., 2011 ; Garland & Howard, 2009 ). Mental health treatment research will be an essential contributor to building knowledge in this area.

Discovering how the brain changes in response to mental health treatment

In addition to understanding where the brain can change, research on mental health interventions can also provide important information on how the brain changes. As the field is learning more about the impact of mental health interventions on different regions of the brain, new findings indicate that multiple avenues exist for improving mental health outcomes through neural change. A primary question for the field at this time is whether brain parameters need to be restored to those equivalent to healthy or unaffected individuals, or whether the brain might compensate and chart completely novel avenues of function and structure to improve outcomes ( Penadés et al., 2017 ). There is evidence from the stroke and rehabilitation literature that compensation may be a common way for the brain to improve function ( Murphy & Corbett, 2009 ). For example, a study by Small, Hlustik, Noll, Genovese, and Solodkin (2002) observed that participants who regained limb function after a motor-impacting stroke had significant increases in motor cortex activity in the hemisphere opposite of the affected area of the brain. This suggested neural compensation: that the brain was rerouting motor activity from the damaged hemisphere to the functional one to improve movement ( Small et al., 2002 ).

In the mental health field, evidence is emerging that both compensatory and restorative changes are possible. For example, in a study of cognitive remediation in people living with schizophrenia, Penadés and colleagues (2013) found that pre-frontal brain function became more normalized and similar to healthy individuals during the course of treatment, although the implications for behavior were less clear. Another study of neural feedback training in children with attention deficit hyperactivity disorder found that individuals could up-regulate a key region of the prefrontal cortex similar to healthy individuals, with significant improvements in hyperactivity and inattentive symptoms ( Alegria et al., 2017 ). Further, a study of age-related memory loss found that strategy-based memory training produced increased brain activity in novel regions not active prior to treatment, suggesting the compensatory recruitment of brain regions to enhance memory function in older adults ( Belleville et al., 2015 ). In addition, a study of children with abuse-related PTSD observed a normalization of frontotemporal activation during the course of CBT, which was associated with reduced emotional arousal; this indicated possible normalization of some aspects of brain function contributing to clinical recovery ( Thomaes et al., 2012 ).

These neuroimaging studies of mental health interventions provide important insights into how the brain changes to improve cognition, psychosocial functioning, and recovery. Although this area of research is in its infancy, evidence exists across a wide range of conditions for the possibility of both restorative and compensatory mechanisms involved in brain change associated with mental health improvement. Understanding whether a neuroimaging effect represents compensation or restoration is heavily dependent upon a firm grasp of neurotypical brain function and abnormalities associated with disorder. Both cognitive and clinical neuroscience are rapidly generating such data, and research from mental health interventions can help determine not only where the brain changes, but also whether normalization results from treatment or whether functional improvements can be gained through compensatory processes.

Linking treatment-related brain change to meaningful behavioral outcomes

From the discussion of how mental health research can inform cognitive neuroscience, it should be increasingly clear that the brain can change as a result of mental health intervention. However, not all changes are meaningful or helpful, and simply discovering that a region is amenable to change will do little to help those living with a disability if such changes are not linked to meaningful outcomes. The integration of mental health treatment research with cognitive neuroscience can reveal how treatment-related brain changes support real-world functioning and recovery, which is likely most pertinent to the underserved populations social workers serve in community mental health clinics.

The use of cognitive neuroscience techniques in mental health treatment research can demonstrate specific locations in the brain altered by intervention that subsequently had a positive influence on symptoms, cognition, and/or functioning. For example, individuals with major depression have difficulty processing emotional information. Such challenges reflect underactivation of frontolimbic regions, which Ritchey, Dolcos, Eddington, Strauman, and Cabeza (2011) demonstrated could be reversed by participating in CBT. After completing an average of 21 CBT sessions, individuals with major depression had significant pre- to posttreatment increases in brain activation in the ventromedial prefrontal cortex, amygdala, caudate, hippocampus, and the anterior temporal lobe while evaluating emotional faces in the scanner. However, only the ventromedial prefrontal cortex and anterior temporal lobe predicted meaningful improvement in depression symptomatology ( Ritchey et al., 2011 ). CBT is a comprehensive program that attempts to address several aspects of thinking (i.e., evaluating automatic thoughts, identifying feelings, and modifying beliefs). Therefore, such results indicate that although other brain areas were alerted by CBT, the ventromedial prefrontal cortex and anterior temporal lobe may have a meaningful link to emotional processing abilities and relieving symptoms of depression—a marked advance in understanding the functional significance of these brain changes.

In individuals with autism, difficulty recognizing emotions in others is a prominent feature of the condition ( Lozier, Vanmeter, & Marsh, 2014 ; Uljarevic & Hamilton, 2013 ). Reduced activation in the fusiform gyrus, a temporal lobe region implicated in facial-affect recognition, is a commonly observed neural correlate of social cognitive impairment in autism ( Corbett et al., 2009 ; Schultz, 2005 ). Bölte et al. (2006) hypothesized that after 5 weeks of facial-affect recognition training, individuals with autism would show significant behavioral improvement in facial-affect recognition associated with increased activity in the fusiform gyrus. However, increased activation in the superior parietal lobule and right medial occipital lobe, not the fusiform gyrus, were significantly correlated with improved behavioral facial-affect recognition skills after training ( Bölte et al., 2006 ). Similarly, a more recent study observed that improved interpersonal communication scores were significantly related to medial prefrontal cortex activation—rather than the assumed fusiform gyrus—following facial-affect recognition training ( Bölte et al., 2015 ). These results further demonstrate the importance of integrating neuroimaging measures into clinical trials to advance our understanding of where change in the brain matters for improving psychosocial functioning and recovery.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive treatment that uses targeted brain stimulation by sending a small but strong electrical current into the cerebral cortex to alter brain functioning. rTMS has treated several mental health conditions ( Slotema, Dirk Blom, Hoek, & Sommer, 2010 ). Given that this treatment is a focused approach for stimulating selected brain regions with the goal to reduce behavioral symptoms, rTMS exemplifies the importance of linking brain change to meaningful outcomes. This treatment has been applied to the positive and negative symptoms of schizophrenia, albeit with mixed findings for efficacy ( Hasan et al., 2017 ; Kimura et al., 2016 ). rTMS treatments traditionally target the left dorsolateral prefrontal cortex to address negative symptoms and the left temporoparietal cortex to reduce positive symptoms in the condition ( Freitas, Fregni, & Pascual-Leone, 2009 ; Shi, Yu, Cheung, Shum, & Chan, 2014 ). Despite target engagement of these theorized regions by rTMS, there often is no observed effect on clinical outcome (i.e., reduced hallucinations). For example, in a double-blind, randomized controlled trial, Novak et al. (2006) applied rTMS to the left dorsolateral prefrontal cortex for 10 days and observed no significant improvements in negative symptom scores. The variability of efficacy findings in rTMS for schizophrenia likely reflects a lack of knowledge of where to target rTMS to optimize the effects on meaningful behavioral change. Such rTMS studies underscore the importance of knowing the exact brain regions that need to be altered by an intervention in order to observe tangible improvements in clinical and functional outcomes in clients.

To truly optimize mental health treatment protocols, however, neuroimaging-based research will need go a step further and examine the degree to which brain changes associated with treatment impact more distal, real-world functioning goals. Preliminary cognitive neuroscience findings indicate that there is a neural signal associated with better social functioning in society ( Wojtalik et al., 2017 ), including financial stability in old age ( Han et al., 2014 ), income level ( Hanson, Chandra, Wolfe, & Pollak, 2011 ), socioeconomic status ( Noble, Houston, Kan, & Sowell, 2012 ), and marriage ( Petrican, Rosenbaum, & Grady, 2015 ). Knowledge of the reciprocal link between changes in the brain and meaningful progress in the ability to hold a competitive job, finish college, and live independently has vital implications for maximizing and personalizing pharmacological, brain stimulation, and psychosocial interventions. All of these outcomes are prominent goals for most clients working with social workers and are pertinent to clients’ overall quality of life ( Eack & Newhill, 2007 ). Conclusively, examining treatment-related brain alterations and their mediating/moderating role in psychosocial recovery is an imperative direction for improving the specificity and precision of mental health treatments to have a real and meaningful influence on clients’ ability to achieve personal goals.

Summary and Conclusions

Mental health conditions are severely and persistently disabling, placing significant barriers on individuals’ ability to recover and achieve personal goals, such as maintaining competitive employment and living independently. Despite significant advances in mental health treatment in both pharmacological and psychosocial arenas, functional recovery from mental health conditions remains inadequate. Such limits in mental health treatment are evidence of the need for novel methods to improve interventions. Recent advances in cognitive neuroscience offer unique opportunities to improve the effectiveness of mental health treatment. This paper reviewed how incorporating cognitive neuroscience into clinical research conducted by social workers and allied mental health professions may improve mental health interventions and promote greater functional recovery ( National Institutes of Health, 2015 ).

We reviewed several developing avenues that demonstrate the clinical utility of combining cognitive neuroscience and mental health treatment research (see Table 1 ). Cognitive neuroscience can improve mental health treatments so they are more preventive, early intervening, and targeted at core pathophysiology. Neuroimaging-based biomarker and treatment-target research can indicate brain signatures of risk and resiliency for mental health conditions or symptoms. Findings from such research directs interventionists to optimal locations in the brain to prevent onset or reduce long-term disability by intervening early in the course of an illness. The goal of linking clients’ neurobiology to treatment protocols is to increase personalization and decrease the lengthy process of finding the right treatment. Additionally, mental health treatment research provides a powerful context for cognitive neuroscience research. The longitudinal nature of clinical trials using neuroimaging measures provides additional information about where , how , and what in the brain can be altered by treatment to promote the greatest opportunities for complete, client-defined recovery from mental illness. Such knowledge will support the field in prioritizing novel and refined treatment strategies to improve interventions for people burdened by some of the most disabling effects of mental illness.

Of course, the brain is not the ultimate solution for developing and optimizing mental health treatments. Readers should be aware of the cautions, limitations, and reductionist nature of cognitive neuroscience methodologies (for a full review see Satel & Lilenfeld, 2013 ). Current neuroimaging tools capture an oversimplified and reduced picture of the intricate complexities of the physiology of the human brain and the black box of the mind. For example, fMRI provides a correlational link between changes in blood flow (i.e., a lighted blob on an image) and a single cognitive ability, such as working memory or empathy. Neurobiological changes during a cognitive paradigm measured with cognitive neuroscience techniques are not a direct representation of what a person is truly thinking or feeling, and it would be premature for researchers to make such claims ( Satel & Lilienfeld, 2013 ). Neuroimaging methods are merely a new and increasingly available tool for social workers to use in their research to address the biopsychosocial contributors to mental health. Our goal with this article is to encourage equal distribution of importance across all three domains of the biopsychosocial framework. Social workers often use psychosocial approaches in mental health treatment, but biology also requires attention to facilitate a comprehensive, holistic view of client circumstances. The integration of social work and cognitive neuroscience has considerable potential to improve the lives of people suffering from mental health conditions by attending to biology, the social environment, and their reciprocal interplay.

The use of cognitive neuroscience to improve mental health treatment also contributes to several of the 12 Grand Challenges for Social Work ( American Academy of Social Work and Social Welfare, n.d. ), such as (a) harnessing technology for social good, (b) advancing long and productive lives, (c) eradicating social isolation, and (d) achieving equal opportunity and justice. For example, DeVylder (2016) proposed that social workers lead collaborative efforts to develop innovative psychosocial treatments to prevent the onset of psychosis aligned with the grand challenge to ensure healthy development for all youth. The application of cognitive neuroscience findings indicating brain areas linked to the conversion of psychosis ( Dazzan et al., 2011 ; Smieskova et al., 2010 ; Thermenos et al., 2016 ) directly informs the development of prevention treatments, is consistent with the biopsychosocial framework, and is essential to the success of this grand challenge.

The integration of cognitive neuroscience and mental health treatment research is an exciting and innovative opportunity for improving intervention effectiveness. However, implementing neuroimaging measures can be intimidating and overwhelming. Social workers may be surprised by the richness of resources and the feasibility of incorporating cognitive neuroscience research. A first step to integrate cognitive neuroscience and mental health treatment research is the inclusion of neuroimaging findings from clinical trials in social work courses, which is already becoming a practice in psychiatry ( Etkin & Cuthbert, 2014 ). For general teaching resources, see Egan, Neely-Barnes, and Combs-Orme (2011) ; Farmer (2008) ; Matto et al. (2014) ; and Shapiro and Applegate (2000) . Additionally, cognitive neuroscience findings can be used as a clinical platform to destigmatize diagnoses and symptoms by educating clients and their family members about the neurobiology of mental health conditions and the ability of treatments to change the brain for the better.

Regarding methods and analysis, software for processing and analyzing neuroimaging data is freely available, such as Statistical Parametric Mapping (SPM; http://www.fil.ion.ucl.ac.uk/spm/resources ) and FSL ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ). SPM requires MATLAB to operate, but it provides an easy-to-use graphical user interface and a detailed, step-by-step manual ( http://www.fil.ion.ucl.ac.uk/spm/doc/ ) with downloadable practice data across several neuroimaging modalities ( http://www.fil.ion.ucl.ac.uk/spm/data/ ). Statistical Analysis of fMRI Data ( Ashby, 2011 ) is a helpful, low-cost textbook that provides a straightforward and easy-to-read foundation for neuroimaging methods and analysis. Cognitive neuroscience training courses also are available for scientists without a traditional neuroscience background (see, for example, Neurometrika at http://neurometrika.org/ ).

Overall, by summarizing the cognitive neuroscience literature related to mental health treatment and providing resources, we hope that social workers and allied professions feel encouraged and capable of beginning to include cognitive neuroscience measures in their intervention studies. The next generation of mental health treatment advances will require such an integration, and a true biopsychosocial understanding of client strengths, challenges, and opportunities for intervention. The integration of cognitive neuroscience and mental health treatment research has the potential to eradicate lifelong disability by increasing the precision of client-driven, first-line treatment decisions.

Acknowledgments

This research was supported by National Institutes of Health grants MH 92440 (Matcheri S. Keshavan and Shaun M. Eack, principal investigators), MH 106450 (Shaun M. Eack, principal investigator), and MH 113277 (Jessica A. Wojtalik, principal investigator).

Contributor Information

Jessica A. Wojtalik, Doctoral candidate at the University of Pittsburgh School of Social Work.

Shaun M. Eack, Professor at the University of Pittsburgh School of Social Work and Department of Psychiatry.

Matthew J. Smith, Associate professor at the University of Michigan School of Social Work.

Matcheri S. Keshavan, Professor in the Harvard Medical School Department of Psychiatry.

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Collection  10 March 2022

Top 100 in Neuroscience

This collection highlights our most downloaded* neuroscience papers published in 2021. Featuring authors from around the world, these papers showcase valuable research from an international community.

*Data obtained from SN Inights, which is based on Digital Science's Dimensions.

Musical components important for the Mozart K448 effect in epilepsy

• Robert J. Quon
• Michael A. Casey
• Barbara C. Jobst

Brain structure changes associated with sexual orientation

• Mikhail Votinov
• Katharina S. Goerlich

Selective time-dependent changes in activity and cell-specific gene expression in human postmortem brain

• Fabien Dachet
• James B. Brown
• Jeffrey A. Loeb

Menopause impacts human brain structure, connectivity, energy metabolism, and amyloid-beta deposition

• Lisa Mosconi
• Valentina Berti
• Roberta Diaz Brinton

Multimodal deep learning models for early detection of Alzheimer’s disease stage

• Janani Venugopalan
• May D. Wang

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

• Ramin Ranjbarzadeh
• Abbas Bagherian Kasgari
• Malika Bendechache

Splitting sleep between the night and a daytime nap reduces homeostatic sleep pressure and enhances long-term memory

• James N. Cousins
• Ruth L. F. Leong
• Michael W. L. Chee

Listening to speech with a guinea pig-to-human brain-to-brain interface

• Claus-Peter Richter
• Petrina La Faire
• Alan G. Micco

Benefit of human moderate running boosting mood and executive function coinciding with bilateral prefrontal activation

• Chorphaka Damrongthai
• Ryuta Kuwamizu
• Hideaki Soya

Enhanced activations in syntax-related regions for multilinguals while acquiring a new language

• Keita Umejima
• Suzanne Flynn
• Kuniyoshi L. Sakai

Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements

• Kaushalya Kumarasinghe
• Nikola Kasabov
• Denise Taylor

Longitudinal effects of meditation on brain resting-state functional connectivity

• Zongpai Zhang
• Wen-Ming Luh
• Weiying Dai

Evidence of a new hidden neural network into deep fasciae

• Caterina Fede
• Lucia Petrelli
• Carla Stecco

Cross-sex hormone treatment and own-body perception: behavioral and brain connectivity profiles

• Behzad S. Khorashad
• Amirhossein Manzouri
• Ivanka Savic

A directional 3D neurite outgrowth model for studying motor axon biology and disease

• Xandor M. Spijkers
• Svetlana Pasteuning-Vuhman
• R. Jeroen Pasterkamp

The role of dorsolateral and ventromedial prefrontal cortex in the processing of emotional dimensions

• Vahid Nejati
• Reyhaneh Majdi
• Michael A. Nitsche

How neurons exploit fractal geometry to optimize their network connectivity

• Julian H. Smith
• Conor Rowland
• R. P. Taylor

Defining early changes in Alzheimer’s disease from RNA sequencing of brain regions differentially affected by pathology

• Boris Guennewig
• Greg T. Sutherland

Narcissistic personality traits and prefrontal brain structure

• Igor Nenadić
• Carsten Lorenz
• Christian Gaser

EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors

• Christopher J. Long
• Tom Francart

Investigating real-life emotions in romantic couples: a mobile EEG study

• Julian Packheiser
• Gesa Berretz
• Sebastian Ocklenburg

Sex-dependent alterations in behavior, drug responses and dopamine transporter expression in heterozygous DAT-Cre mice

• Kauê Machado Costa
• Daniela Schenkel
• Jochen Roeper

Functional network connectivity during Jazz improvisation

• Victor M. Vergara
• Martin Norgaard
• Vince D. Calhoun

Buspirone alleviates anxiety, depression, and colitis; and modulates gut microbiota in mice

• Jeon-Kyung Kim
• Sang-Kap Han
• Dong-Hyun Kim

Human cerebral organoids as a therapeutic drug screening model for Creutzfeldt–Jakob disease

• Bradley R. Groveman
• Natalia C. Ferreira
• Cathryn L. Haigh

Thioflavin-positive tau aggregates complicating quantification of amyloid plaques in the brain of 5XFAD transgenic mouse model

• YoungSoo Kim

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

• Mona Nasseri
• Tal Pal Attia
• Benjamin H. Brinkmann

Post-traumatic seizures and antiepileptic therapy as predictors of the functional outcome in patients with traumatic brain injury

• Valeria Pingue
• Chiara Mele
• Antonio Nardone

Human α-synuclein overexpression in a mouse model of Parkinson’s disease leads to vascular pathology, blood brain barrier leakage and pericyte activation

• Osama Elabi
• Abderahim Gaceb
• Gesine Paul

Neuropsychiatric profiles and conversion to dementia in mild cognitive impairment, a latent class analysis

• Natalia Roberto
• Maria J. Portella
• Sergi Valero

Comparative neuroanatomy of the lumbosacral spinal cord of the rat, cat, pig, monkey, and human

• Amirali Toossi
• Bradley Bergin
• Vivian K. Mushahwar

A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction

• Mauro. F. Pinto
• Adriana Leal
• César A. Teixeira

The effects of positive or negative self-talk on the alteration of brain functional connectivity by performing cognitive tasks

• Junhyung Kim
• Joon Hee Kwon
• Jae-Jin Kim

Deep learning-Based 3D inpainting of brain MR images

• Seung Kwan Kang
• Seong A. Shin
• Jae Sung Lee

Both high fat and high carbohydrate diets impair vagus nerve signaling of satiety

• Hailley Loper
• Monique Leinen
• Matthew A. Schiefer

PINK1 deficiency impairs adult neurogenesis of dopaminergic neurons

• Sarah J. Brown
• Ibrahim Boussaad
• Oliver Bandmann

Altered spontaneous activity in the frontal gyrus in dry eye: a resting-state functional MRI study

Online and offline effects of transcranial alternating current stimulation of the primary motor cortex

• Ivan Pozdniakov
• Alicia Nunez Vorobiova
• Matteo Feurra

Cell type-specific changes in transcriptomic profiles of endothelial cells, iPSC-derived neurons and astrocytes cultured on microfluidic chips

• H. H. T. Middelkamp
• A. H. A. Verboven
• A. D. van der Meer

Brain MRI in SARS-CoV-2 pneumonia patients with newly developed neurological manifestations suggestive of brain involvement

• Batil Alonazi
• Ahmed M. Farghaly
• Mustafa Z. Mahmoud

Diazepam causes sedative rather than anxiolytic effects in C57BL/6J mice

• Marina Pádua-Reis
• Diana Aline Nôga
• Martina Blunder

Photons detected in the active nerve by photographic technique

• Andrea Zangari
• Davide Micheli
• Maria Emiliana Caristo

Cannabidiol induces autophagy via ERK1/2 activation in neural cells

• Talita A. M. Vrechi
• Anderson H. F. F. Leão
• Gustavo J. S. Pereira

Deep learning-based pupil model predicts time and spectral dependent light responses

• Babak Zandi
• Tran Quoc Khanh

ATP signaling in the integrative neural center of Aplysia californica

• János Györi
• Andrea B. Kohn
• Leonid L. Moroz

EEG signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

• Samaneh A. Mofrad
• Astri J. Lundervold
• Alexander S. Lundervold

Characterization of mitochondrial health from human peripheral blood mononuclear cells to cerebral organoids derived from induced pluripotent stem cells

• Angela Duong
• Alesya Evstratova
• Ana C. Andreazza

Astrocytic expression of the Alzheimer’s disease risk allele, ApoEε4, potentiates neuronal tau pathology in multiple preclinical models

• Angela Marie Jablonski
• Bhavya Voleti

Effects of the sigma-1 receptor agonist blarcamesine in a murine model of fragile X syndrome: neurobehavioral phenotypes and receptor occupancy

• Samantha T. Reyes
• Robert M. J. Deacon
• Frederick T. Chin

Magnetic domains oscillation in the brain with neurodegenerative disease

• Gunther Kletetschka
• Robert Bazala
• Eva Svecova

Right frontal anxiolytic-sensitive EEG ‘theta’ rhythm in the stop-signal task is a theory-based anxiety disorder biomarker

• Shabah M. Shadli
• Lynne C. Ando
• Neil McNaughton

Role of miRNAs shuttled by mesenchymal stem cell-derived small extracellular vesicles in modulating neuroinflammation

• Debora Giunti
• Chiara Marini
• Antonio Uccelli

Position sensitive measurement of trace lithium in the brain with NIK (neutron-induced coincidence method) in suicide

• J. Schoepfer
• R. Gernhäuser

Major oscillations in spontaneous home-cage activity in C57BL/6 mice housed under constant conditions

• Karin Pernold
• Eric Rullman
• Brun Ulfhake

Involvement of the dopaminergic system in the reward-related behavior of pregabalin

• Yusuf S. Althobaiti
• Farooq M. Almutairi
• Zahoor A. Shah

A high-density electroencephalography study reveals abnormal sleep homeostasis in patients with rapid eye movement sleep behavior disorder

• Amandine Valomon
• Brady A. Riedner
• Melanie Boly

MEG signatures of long-term effects of agreement and disagreement with the majority

• V. Klucharev
• A. Shestakova

Stroke prediction in patients presenting with isolated dizziness in the emergency department

• June-sung Kim
• Hong Jun Bae
• Won Young Kim

Age-dependent and region-specific alteration of parvalbumin neurons, perineuronal nets and microglia in the mouse prefrontal cortex and hippocampus following obesogenic diet consumption

• Amy C. Reichelt
• Claire A. Lemieux
• Lisa M. Saksida

Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings

• Richárd Fiáth
• Domokos Meszéna
• István Ulbert

Amplification of potential thermogenetic mechanisms in cetacean brains compared to artiodactyl brains

• Paul R. Manger
• Nina Patzke

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

• Marion I. van den Heuvel
• Jasmine L. Hect
• Moriah E. Thomason

An anomaly detection approach to identify chronic brain infarcts on MRI

• Kees M. van Hespen
• Jaco J. M. Zwanenburg
• Hugo J. Kuijf

The neurodynamic treatment induces biological changes in sensory and motor neurons in vitro

• Giacomo Carta
• Giovanna Gambarotta
• Federica Fregnan

Optimal flickering light stimulation for entraining gamma waves in the human brain

• Kanghee Lee
• Yeseung Park
• Ki Woong Kim

Two-photon GCaMP6f imaging of infrared neural stimulation evoked calcium signals in mouse cortical neurons in vivo

• Attila Kaszas
• Gergely Szalay
• David Moreau

The Aβ(1–38) peptide is a negative regulator of the Aβ(1–42) peptide implicated in Alzheimer disease progression

• Maa O. Quartey
• Jennifer N. K. Nyarko
• Darrell D. Mousseau

Drinking coffee enhances neurocognitive function by reorganizing brain functional connectivity

• Sung Hoon Kang
• Jung Bin Kim

The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

• Matteo Marucci
• Gianluca Di Flumeri
• Pietro Aricò

CuATSM improves motor function and extends survival but is not tolerated at a high dose in SOD1 G93A mice with a C57BL/6 background

• Jeremy S. Lum
• Mikayla L. Brown
• Justin J. Yerbury

Underlying neurological mechanisms associated with symptomatic convergence insufficiency

• Tara L. Alvarez
• Mitchell Scheiman
• Bharat B. Biswal

Paternal exposure to a common pharmaceutical (Ritalin) has transgenerational effects on the behaviour of Trinidadian guppies

• Alex R. De Serrano
• Kimberly A. Hughes
• F. Helen Rodd

Behavioral arrest and a characteristic slow waveform are hallmark responses to selective 5-HT 2A receptor activation

• April Contreras
• Matthew Khumnark
• Dustin J. Hines

Focused ultrasound mediated blood–brain barrier opening is safe and feasible in a murine pontine glioma model

• Zachary K. Englander
• Hong-Jian Wei
• Cheng-Chia Wu

Basmisanil, a highly selective GABA A -α5 negative allosteric modulator: preclinical pharmacology and demonstration of functional target engagement in man

• Joerg F. Hipp
• Frederic Knoflach
• Maria-Clemencia Hernandez

Spatial memory deficiency early in 6xTg Alzheimer’s disease mouse model

• Shinwoo Kang
• Keun-A Chang

The gut microbiome is associated with brain structure and function in schizophrenia

Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

• Abhishek Uday Patil
• Sejal Ghate
• Chih-Mao Huang

Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit

• Anil K. Palepu
• Aditya Murali
• Robert D. Stevens

LSD-stimulated behaviors in mice require β-arrestin 2 but not β-arrestin 1

• Ramona M. Rodriguiz
• Vineet Nadkarni
• William C. Wetsel

Modified wavelet analysis of ECoG-pattern as promising tool for detection of the blood–brain barrier leakage

• Anastasiya Runnova
• Maksim Zhuravlev
• Jurgen Kurths

Adrenergic inhibition facilitates normalization of extracellular potassium after cortical spreading depolarization

• Hiromu Monai
• Shinnosuke Koketsu
• Hajime Hirase

An atlas for human brain myelin content throughout the adult life span

• Adam V. Dvorak
• Taylor Swift-LaPointe
• Shannon H. Kolind

Immediate and after effects of transcranial direct-current stimulation in the mouse primary somatosensory cortex

• Carlos A. Sánchez-León
• Isabel Cordones
• Javier Márquez-Ruiz

Human brain dynamics in active spatial navigation

• Tien-Thong Nguyen Do
• Chin-Teng Lin
• Klaus Gramann

Reading without phonology: ERP evidence from skilled deaf readers of Spanish

• Brendan Costello
• Sendy Caffarra
• Manuel Carreiras

Alterations in reward network functional connectivity are associated with increased food addiction in obese individuals

• Soumya Ravichandran
• Ravi R. Bhatt
• Arpana Gupta

Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia

• Soo Hyun Cho
• Sookyoung Woo
• Sang Won Seo

The structure dilemma in biological and artificial neural networks

• Thomas Pircher
• Bianca Pircher
• Andreas Feigenspan

Voluntary intake of psychoactive substances is regulated by the dopamine receptor Dop1R1 in Drosophila

• Shun Hiramatsu
• Toshiharu Ichinose

The contribution of platelets to peripheral BDNF elevation in children with autism spectrum disorder

• Cristan A. Farmer
• Audrey E. Thurm
• Joan C. Han

Dopamine-loaded nanoparticle systems circumvent the blood–brain barrier restoring motor function in mouse model for Parkinson’s Disease

• Victoria Monge-Fuentes
• Andréia Biolchi Mayer
• Márcia Renata Mortari

NK1 antagonists attenuate tau phosphorylation after blast and repeated concussive injury

• Frances Corrigan
• Ibolja Cernak
• Robert Vink

Nicotine suppresses Parkinson’s disease like phenotypes induced by Synphilin-1 overexpression in Drosophila melanogaster by increasing tyrosine hydroxylase and dopamine levels

• Angel Carvajal-Oliveros
• Carmen Domínguez-Baleón
• Enrique Reynaud

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer’s disease

• Maurizio Bergamino
• Ryan R. Walsh
• Ashley M. Stokes

A functional spiking neuronal network for tactile sensing pathway to process edge orientation

• Adel Parvizi-Fard
• Mahmood Amiri
• Nitish V. Thakor

The difficulty to model Huntington’s disease in vitro using striatal medium spiny neurons differentiated from human induced pluripotent stem cells

• Kim Le Cann
• Alec Foerster
• Angelika Lampert

Hippocampus-retrosplenial cortex interaction is increased during phasic REM and contributes to memory consolidation

• Daniel Gomes de Almeida-Filho
• Bruna Del Vechio Koike
• Claudio Marcos Queiroz

Machine learning-based classification of mitochondrial morphology in primary neurons and brain

• Garrett M. Fogo
• Anthony R. Anzell
• Thomas H. Sanderson

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