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Lapish CC. Understanding How Acute Alcohol Impacts Neural Encoding in the Rodent Brain. Curr Top Behav Neurosci 2024. [PMID: 38858298 DOI: 10.1007/7854_2024_479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Alcohol impacts neural circuitry throughout the brain and has wide-ranging effects on the biophysical properties of neurons in these circuits. Articulating how these wide-ranging effects might eventually result in altered computational properties has the potential to provide a tractable working model of how alcohol alters neural encoding. This chapter reviews what is currently known about how acute alcohol influences neural activity in cortical, hippocampal, and dopaminergic circuits as these have been the primary focus of understanding how alcohol alters neural computation. While other neural systems have been the focus of exhaustive work on this topic, these brain regions are the ones where in vivo neural recordings are available, thus optimally suited to make the link between changes in neural activity and behavior. Rodent models have been key in developing an understanding of how alcohol impacts the function of these circuits, and this chapter therefore focuses on work from mice and rats. While progress has been made, it is critical to understand the challenges and caveats associated with experimental procedures, especially when performed in vivo, which are designed to answer this question and if/how to translate these data to humans. The hypothesis is discussed that alcohol impairs the ability of neural circuits to acquire states of neural activity that are transiently elevated and characterized by increased complexity. It is hypothesized that these changes are distinct from the traditional view of alcohol being a depressant of neural activity in the forebrain.
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Affiliation(s)
- Christopher C Lapish
- Department of Anatomy, Cell Biology, and Physiology, Stark Neuroscience Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
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2
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Shabestari PS, Zendehrouh S, Ahmadi A, Jafari S, Parvaresh N, Eslami M. Analyzing the network of parent-rated ADHD symptoms before and 2 weeks after the onset of pharmaceutical treatment. JOURNAL OF CHILD AND ADOLESCENT PSYCHIATRIC NURSING 2023; 36:269-277. [PMID: 37157949 DOI: 10.1111/jcap.12421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 04/15/2023] [Accepted: 04/26/2023] [Indexed: 05/10/2023]
Abstract
PROBLEM Via a network analysis approach, following 2 weeks of the medication Ritalin, the present study investigated the quality of symptom interactions and the pattern of behavior changes to identify locations of functional weaknesses in the network interactions of symptomology. METHODS Ritalin® prescribed for 112 children (aged 4-14) with attention deficit hyperactivity disorder (ADHD) as diagnosed by five child and adolescent psychiatrists. Their parents completed Swanson, Nolan, and Pelham-IV questionnaire (SNAP-IV) before and after Ritalin® onset as the pre and post-test, respectively. Then, the network analysis approach was used to discover the pattern of changes in symptom interactions. FINDINGS The results indicated that in 2 weeks following its initiation, Ritalin significantly reduced restlessness and interactions between symptoms of impulsivity. "Inability to follow instructions" and "difficulty waiting their turn" symptoms were the most central symptoms of strength. Three symptoms, "Often has difficulty waiting their turn," "runs and climbs in situations where it is inappropriate" and "does not follow through on instructions," had the most expected influence. In the 14-day period of investigation, Ritalin® was effective in breaking some interactions and components of ADHD, but no significant mitigation of other components of the detected symptomatology network. CONCLUSION Follow-up investigations using network analysis can clarify the dynamics of the network changes after initiation of medications.
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Affiliation(s)
| | - Sareh Zendehrouh
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Atefeh Ahmadi
- Department of Counselling in Midwifery, Neurology Research Center, Razi Faculty of Nursing and Midwifery, Kerman University of Medical Sciences, Kerman, Iran
| | - Sajad Jafari
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Nooshin Parvaresh
- Department of Psychiatry, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahin Eslami
- Department of Psychiatry, Kerman University of Medical Sciences, Kerman, Iran
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Gómez-Carrillo A, Kirmayer LJ. A cultural-ecosocial systems view for psychiatry. Front Psychiatry 2023; 14:1031390. [PMID: 37124258 PMCID: PMC10133725 DOI: 10.3389/fpsyt.2023.1031390] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/08/2023] [Indexed: 05/02/2023] Open
Abstract
While contemporary psychiatry seeks the mechanisms of mental disorders in neurobiology, mental health problems clearly depend on developmental processes of learning and adaptation through ongoing interactions with the social environment. Symptoms or disorders emerge in specific social contexts and involve predicaments that cannot be fully characterized in terms of brain function but require a larger social-ecological view. Causal processes that result in mental health problems can begin anywhere within the extended system of body-person-environment. In particular, individuals' narrative self-construal, culturally mediated interpretations of symptoms and coping strategies as well as the responses of others in the social world contribute to the mechanisms of mental disorders, illness experience, and recovery. In this paper, we outline the conceptual basis and practical implications of a hierarchical ecosocial systems view for an integrative approach to psychiatric theory and practice. The cultural-ecosocial systems view we propose understands mind, brain and person as situated in the social world and as constituted by cultural and self-reflexive processes. This view can be incorporated into a pragmatic approach to clinical assessment and case formulation that characterizes mechanisms of pathology and identifies targets for intervention.
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Affiliation(s)
- Ana Gómez-Carrillo
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, QC, Canada
- Culture and Mental Health Research Unit, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
| | - Laurence J. Kirmayer
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, QC, Canada
- Culture and Mental Health Research Unit, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada
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Gauld C, Depannemaecker D. Dynamical systems in computational psychiatry: A toy-model to apprehend the dynamics of psychiatric symptoms. Front Psychol 2023; 14:1099257. [PMID: 36844296 PMCID: PMC9945965 DOI: 10.3389/fpsyg.2023.1099257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/06/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction These last years, scientific research focuses on the dynamical aspects of psychiatric disorders and their clinical significance. In this article, we proposed a theoretical framework formalized as a generic mathematical model capturing the heterogeneous individual evolutions of psychiatric symptoms. The first goal of this computational model based on differential equations is to illustrate the nonlinear dynamics of psychiatric symptoms. It offers an original approach to nonlinear dynamics to clinical psychiatrists. Methods In this study, we propose a 3+1 dimensions model (x, y, z + f) reproducing the clinical observations encountered in clinical psychiatry with: a variable modeling environmental noise (z) on the patient's internal factors (y) with its temporal specificities (f) and symptomatology (x). This toy-model is able to integrate empirical or simulated data from the influence of perceived environmental over time, their potential importance on the internal and subjective patient-specific elements, and their interaction with the apparent intensity of symptoms. Results Constrained by clinical observation of case formulations, the dynamics of psychiatric symptoms is studied through four main psychiatric conditions were modeled: i) a healthy situation, ii) a kind of psychiatric disorder evolving following an outbreak (i.e., schizophrenia spectrum), iii) a kind of psychiatric disorder evolving by kindling and bursts (e.g., bipolar and related disorders); iv) and a kind of psychiatric disorder evolving due to its high susceptibility to the environment (e.g., spersistent complex bereavement disorder). Moreover, we simulate the action of treatments on different psychiatric conditions. Discussion We show that the challenges of dynamical systems allow to understand the interactions of psychiatric symptoms with environmental, descriptive, subjective or biological variables. Although this non-linear dynamical model has limitations (e.g., explanatory scope or discriminant validity), simulations provide at least five main interests for clinical psychiatry, such as a visualization of the potential different evolution of psychiatric disorders, formulation of clinical cases, information about attracting states and bifurcations, or the possibility of a nosological refinement of psychiatric models (e.g., staging and symptom network models).
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Affiliation(s)
- Christophe Gauld
- Department of Child Psychiatry, University Hospital Lyon, Lyon, France,Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS, Université Claude Bernard Lyon 1, Lyon, France,*Correspondence: Christophe Gauld ✉
| | - Damien Depannemaecker
- Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France,Aix-Marseille University, INSERM, Institut de Neuroscience des Systèmes (INS), Marseille, France,Damien Depannemaecker ✉
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Sherif MA, Fotros A, Greenberg BD, McLaughlin NCR. Understanding cingulotomy's therapeutic effect in OCD through computer models. Front Integr Neurosci 2023; 16:889831. [PMID: 36704759 PMCID: PMC9871832 DOI: 10.3389/fnint.2022.889831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023] Open
Abstract
Cingulotomy is therapeutic in OCD, but what are the possible mechanisms? Computer models that formalize cortical OCD abnormalities and anterior cingulate cortex (ACC) function can help answer this. At the neural dynamics level, cortical dynamics in OCD have been modeled using attractor networks, where activity patterns resistant to change denote the inability to switch to new patterns, which can reflect inflexible thinking patterns or behaviors. From that perspective, cingulotomy might reduce the influence of difficult-to-escape ACC attractor dynamics on other cortical areas. At the functional level, computer formulations based on model-free reinforcement learning (RL) have been used to describe the multitude of phenomena ACC is involved in, such as tracking the timing of expected outcomes and estimating the cost of exerting cognitive control and effort. Different elements of model-free RL models of ACC could be affected by the inflexible cortical dynamics, making it challenging to update their values. An agent can also use a world model, a representation of how the states of the world change, to plan its actions, through model-based RL. OCD has been hypothesized to be driven by reduced certainty of how the brain's world model describes changes. Cingulotomy might improve such uncertainties about the world and one's actions, making it possible to trust the outcomes of these actions more and thus reduce the urge to collect more sensory information in the form of compulsions. Connecting the neural dynamics models with the functional formulations can provide new ways of understanding the role of ACC in OCD, with potential therapeutic insights.
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Affiliation(s)
- Mohamed A. Sherif
- Department of Psychiatry, Brown University, Providence, RI, United States,Carney Institute for Brain Science, Brown University, Providence, RI, United States,Department of Psychiatry Lifespan Health System, Providence, RI, United States,*Correspondence: Mohamed A. Sherif,
| | - Aryandokht Fotros
- Department of Psychiatry, Brown University, Providence, RI, United States,Department of Psychiatry Lifespan Health System, Providence, RI, United States
| | - Benjamin D. Greenberg
- Department of Psychiatry, Brown University, Providence, RI, United States,Carney Institute for Brain Science, Brown University, Providence, RI, United States,Butler Hospital, Providence, RI, United States,United States Department of Veterans Affairs, Providence VA Medical Center, Providence, RI, United States
| | - Nicole C. R. McLaughlin
- Department of Psychiatry, Brown University, Providence, RI, United States,Carney Institute for Brain Science, Brown University, Providence, RI, United States,Butler Hospital, Providence, RI, United States
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John YJ, Sawyer KS, Srinivasan K, Müller EJ, Munn BR, Shine JM. It's about time: Linking dynamical systems with human neuroimaging to understand the brain. Netw Neurosci 2022; 6:960-979. [PMID: 36875012 PMCID: PMC9976648 DOI: 10.1162/netn_a_00230] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/04/2022] [Indexed: 11/04/2022] Open
Abstract
Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.
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Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA
| | - Kayle S. Sawyer
- Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Boston VA Healthcare System, Boston, MA, USA
- Sawyer Scientific, LLC, Boston, MA, USA
| | - Karthik Srinivasan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eli J. Müller
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - Brandon R. Munn
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
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Spee BTM, Sladky R, Fingerhut J, Laciny A, Kraus C, Carls-Diamante S, Brücke C, Pelowski M, Treven M. Repeating patterns: Predictive processing suggests an aesthetic learning role of the basal ganglia in repetitive stereotyped behaviors. Front Psychol 2022; 13:930293. [PMID: 36160532 PMCID: PMC9497189 DOI: 10.3389/fpsyg.2022.930293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Recurrent, unvarying, and seemingly purposeless patterns of action and cognition are part of normal development, but also feature prominently in several neuropsychiatric conditions. Repetitive stereotyped behaviors (RSBs) can be viewed as exaggerated forms of learned habits and frequently correlate with alterations in motor, limbic, and associative basal ganglia circuits. However, it is still unclear how altered basal ganglia feedback signals actually relate to the phenomenological variability of RSBs. Why do behaviorally overlapping phenomena sometimes require different treatment approaches−for example, sensory shielding strategies versus exposure therapy for autism and obsessive-compulsive disorder, respectively? Certain clues may be found in recent models of basal ganglia function that extend well beyond action selection and motivational control, and have implications for sensorimotor integration, prediction, learning under uncertainty, as well as aesthetic learning. In this paper, we systematically compare three exemplary conditions with basal ganglia involvement, obsessive-compulsive disorder, Parkinson’s disease, and autism spectrum conditions, to gain a new understanding of RSBs. We integrate clinical observations and neuroanatomical and neurophysiological alterations with accounts employing the predictive processing framework. Based on this review, we suggest that basal ganglia feedback plays a central role in preconditioning cortical networks to anticipate self-generated, movement-related perception. In this way, basal ganglia feedback appears ideally situated to adjust the salience of sensory signals through precision weighting of (external) new sensory information, relative to the precision of (internal) predictions based on prior generated models. Accordingly, behavioral policies may preferentially rely on new data versus existing knowledge, in a spectrum spanning between novelty and stability. RSBs may then represent compensatory or reactive responses, respectively, at the opposite ends of this spectrum. This view places an important role of aesthetic learning on basal ganglia feedback, may account for observed changes in creativity and aesthetic experience in basal ganglia disorders, is empirically testable, and may inform creative art therapies in conditions characterized by stereotyped behaviors.
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Affiliation(s)
- Blanca T. M. Spee
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Nijmegen, Netherlands
| | - Ronald Sladky
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Joerg Fingerhut
- Berlin School of Mind and Brain, Department of Philosophy, Humboldt-Universität zu Berlin, Berlin, Germany
- Faculty of Philosophy, Philosophy of Science and Religious Studies, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Alice Laciny
- Konrad Lorenz Institute for Evolution and Cognition Research, Klosterneuburg, Austria
| | - Christoph Kraus
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Medical Neuroscience Cluster, Medical University of Vienna, Vienna, Austria
| | | | - Christof Brücke
- Medical Neuroscience Cluster, Medical University of Vienna, Vienna, Austria
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Matthew Pelowski
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Marco Treven
- Konrad Lorenz Institute for Evolution and Cognition Research, Klosterneuburg, Austria
- Medical Neuroscience Cluster, Medical University of Vienna, Vienna, Austria
- Department of Neurology, Medical University of Vienna, Vienna, Austria
- *Correspondence: Marco Treven,
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Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning. Proc Natl Acad Sci U S A 2022; 119:e2121331119. [PMID: 35622896 DOI: 10.1073/pnas.2121331119] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
SignificanceAdolescence is a period during which there are important changes in behavior and the structure of the brain. In this manuscript, we use theoretical modeling to show how improvements in working memory and reinforcement learning that occur during adolescence can be explained by the reduction in synaptic connectivity in prefrontal cortex that occurs during a similar period. We train recurrent neural networks to solve working memory and reinforcement learning tasks and show that when we prune connectivity in these networks, they perform the tasks better. The improvement in task performance, however, can come at the cost of flexibility as the pruned networks are not able to learn some new tasks as well.
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A. Markovics J. Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback. ARTIF INTELL 2022. [DOI: 10.5772/intechopen.98343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.
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Thome J, Steinbach R, Grosskreutz J, Durstewitz D, Koppe G. Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics. Hum Brain Mapp 2022; 43:681-699. [PMID: 34655259 PMCID: PMC8720197 DOI: 10.1002/hbm.25679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 09/27/2021] [Indexed: 12/19/2022] Open
Abstract
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS.
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Affiliation(s)
- Janine Thome
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
- Clinic for Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
| | - Robert Steinbach
- Hans Berger Department of NeurologyJena University HospitalJenaGermany
| | - Julian Grosskreutz
- Precision Neurology, Department of NeurologyUniversity of LuebeckLuebeckGermany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
| | - Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
- Clinic for Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
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11
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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12
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Smucny J, Shi G, Davidson I. Deep Learning in Neuroimaging: Overcoming Challenges With Emerging Approaches. Front Psychiatry 2022; 13:912600. [PMID: 35722548 PMCID: PMC9200984 DOI: 10.3389/fpsyt.2022.912600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/06/2022] [Indexed: 12/12/2022] Open
Abstract
Deep learning (DL) is of great interest in psychiatry due its potential yet largely untapped ability to utilize multidimensional datasets (such as fMRI data) to predict clinical outcomes. Typical DL methods, however, have strong assumptions, such as large datasets and underlying model opaqueness, that are suitable for natural image prediction problems but not medical imaging. Here we describe three relatively novel DL approaches that may help accelerate its incorporation into mainstream psychiatry research and ultimately bring it into the clinic as a prognostic tool. We first introduce two methods that can reduce the amount of training data required to develop accurate models. These may prove invaluable for fMRI-based DL given the time and monetary expense required to acquire neuroimaging data. These methods are (1) transfer learning - the ability of deep learners to incorporate knowledge learned from one data source (e.g., fMRI data from one site) and apply it toward learning from a second data source (e.g., data from another site), and (2) data augmentation (via Mixup) - a self-supervised learning technique in which "virtual" instances are created. We then discuss explainable artificial intelligence (XAI), i.e., tools that reveal what features (and in what combinations) deep learners use to make decisions. XAI can be used to solve the "black box" criticism common in DL and reveal mechanisms that ultimately produce clinical outcomes. We expect these techniques to greatly enhance the applicability of DL in psychiatric research and help reveal novel mechanisms and potential pathways for therapeutic intervention in mental illness.
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Affiliation(s)
- Jason Smucny
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, CA, United States
| | - Ge Shi
- Department of Computer Sciences, University of California, Davis, Davis, CA, United States
| | - Ian Davidson
- Department of Computer Sciences, University of California, Davis, Davis, CA, United States
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13
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Morningstar MD, Barnett WH, Goodlett CR, Kuznetsov A, Lapish CC. Understanding ethanol's acute effects on medial prefrontal cortex neural activity using state-space approaches. Neuropharmacology 2021; 198:108780. [PMID: 34480911 PMCID: PMC8488975 DOI: 10.1016/j.neuropharm.2021.108780] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/10/2021] [Accepted: 08/30/2021] [Indexed: 12/22/2022]
Abstract
Acute ethanol (EtOH) intoxication results in several maladaptive behaviors that may be attributable, in part, to the effects of EtOH on neural activity in medial prefrontal cortex (mPFC). The acute effects of EtOH on mPFC function have been largely described as inhibitory. However, translating these observations on function into a mechanism capable of delineating acute EtOH's effects on behavior has proven difficult. This review highlights the role of acute EtOH on electrophysiological measurements of mPFC function and proposes that interpreting these changes through the lens of dynamical systems theory is critical to understand the mechanisms that mediate the effects of EtOH intoxication on behavior. Specifically, the present review posits that the effects of EtOH on mPFC N-methyl-d-aspartate (NMDA) receptors are critical for the expression of impaired behavior following EtOH consumption. This hypothesis is based on the observation that recurrent activity in cortical networks is supported by NMDA receptors, and, when disrupted, may lead to impairments in cognitive function. To evaluate this hypothesis, we discuss the representation of mPFC neural activity in low-dimensional, dynamic state spaces. This approach has proven useful for identifying the underlying computations necessary for the production of behavior. Ultimately, we hypothesize that EtOH-related alterations to NMDA receptor function produces alterations that can be effectively conceptualized as impairments in attractor dynamics and provides insight into how acute EtOH disrupts forms of cognition that rely on mPFC function. This article is part of the special Issue on 'Neurocircuitry Modulating Drug and Alcohol Abuse'.
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Affiliation(s)
| | - William H Barnett
- Indiana University-Purdue University Indianapolis, Department of Psychology, USA
| | - Charles R Goodlett
- Indiana University-Purdue University Indianapolis, Department of Psychology, USA; Indiana University School of Medicine, Stark Neurosciences, USA
| | - Alexey Kuznetsov
- Indiana University-Purdue University Indianapolis, Department of Mathematics, USA; Indiana University School of Medicine, Stark Neurosciences, USA
| | - Christopher C Lapish
- Indiana University-Purdue University Indianapolis, Department of Psychology, USA; Indiana University School of Medicine, Stark Neurosciences, USA
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14
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Iravani B, Arshamian A, Fransson P, Kaboodvand N. Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy. Neuroimage 2021; 231:117844. [PMID: 33577937 DOI: 10.1016/j.neuroimage.2021.117844] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 01/13/2023] Open
Abstract
Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individual's empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.
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Affiliation(s)
- Behzad Iravani
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden.
| | - Artin Arshamian
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Neda Kaboodvand
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
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15
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Kim CS. Bayesian mechanics of perceptual inference and motor control in the brain. BIOLOGICAL CYBERNETICS 2021; 115:87-102. [PMID: 33471182 PMCID: PMC7925488 DOI: 10.1007/s00422-021-00859-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
The free energy principle (FEP) in the neurosciences stipulates that all viable agents induce and minimize informational free energy in the brain to fit their environmental niche. In this study, we continue our effort to make the FEP a more physically principled formalism by implementing free energy minimization based on the principle of least action. We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim in Neural Comput 30:2616-2659, 2018, https://doi.org/10.1162/neco_a_01115 ) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton's equation of motion and subject to the control signal arising from the brain's prediction errors at the proprioceptive level. To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations.
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Affiliation(s)
- Chang Sub Kim
- Department of Physics, Chonnam National University, Gwangju, 61186, Republic of Korea.
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16
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Szalisznyó K, Silverstein DN. Computational Predictions for OCD Pathophysiology and Treatment: A Review. Front Psychiatry 2021; 12:687062. [PMID: 34658945 PMCID: PMC8517225 DOI: 10.3389/fpsyt.2021.687062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/01/2021] [Indexed: 01/29/2023] Open
Abstract
Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.
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Affiliation(s)
- Krisztina Szalisznyó
- Department of Neuroscience and Psychiatry, Uppsala University Hospital, Uppsala, Sweden.,Theoretical Neuroscience Group, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary
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17
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Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2021; 46:176-190. [PMID: 32668442 PMCID: PMC7689428 DOI: 10.1038/s41386-020-0767-z] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/04/2020] [Accepted: 07/06/2020] [Indexed: 02/06/2023]
Abstract
Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored treatments. To this end, it appears that the analysis of 'small' experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather 'small' samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.
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Affiliation(s)
- Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany.
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany.
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany.
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany.
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18
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Huys QJM, Browning M, Paulus MP, Frank MJ. Advances in the computational understanding of mental illness. Neuropsychopharmacology 2021; 46:3-19. [PMID: 32620005 PMCID: PMC7688938 DOI: 10.1038/s41386-020-0746-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/11/2020] [Accepted: 06/15/2020] [Indexed: 12/11/2022]
Abstract
Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.
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Affiliation(s)
- Quentin J M Huys
- Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
- Camden and Islington NHS Trust, London, UK.
| | - Michael Browning
- Computational Psychiatry Lab, Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Trust, Oxford, UK
| | - Martin P Paulus
- Laureate Institute For Brain Research (LIBR), Tulsa, OK, USA
| | - Michael J Frank
- Cognitive, Linguistic & Psychological Sciences, Neuroscience Graduate Program, Brown University, Providence, RI, USA
- Carney Center for Computational Brain Science, Carney Institute for Brain Science Psychiatry and Human Behavior, Brown University, Providence, RI, USA
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