Although there have been tremendous advances in the understanding of human dysfunctions in the brain circuitry for self-reflection, emotion, and cognitive control, a brain-based taxonomy for mental ...disease is still lacking. As a result, these advances have not been translated into actionable clinical tools, and the language of brain circuits has not been incorporated into training programmes. To address this gap, I present this synthesis of published work, with a focus on functional imaging of circuit dysfunctions across the spectrum of mood and anxiety disorders. This synthesis provides the foundation for a taxonomy of putative types of dysfunction, which cuts across traditional diagnostic boundaries for depression and anxiety and includes instead distinct types of neural circuit dysfunction that together reflect the heterogeneity of depression and anxiety. This taxonomy is suited to specifying symptoms in terms of underlying neural dysfunction at the individual level and is intended as the foundation for building mechanistic research and ultimately guiding clinical practice.
Complex emotional, cognitive and self‐reflective functions rely on the activation and connectivity of large‐scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a ...taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlines the current consensus as to what constitutes the organization of large‐scale circuits in the human brain identified using parcellation and meta‐analysis. The focus is on neural circuits implicated in resting reflection (default mode), detection of “salience,” affective processing (“threat” and “reward”), “attention,” and “cognitive control.” Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders is reviewed, with an emphasis on published meta‐analyses and reviews of circuit dysfunctions that have been identified in at least two well‐powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit‐defined “biotypes.” In this framework, biotypes are defined by profiles of extent of dysfunction on each large‐scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world.
Information processing during human cognitive and emotional operations is thought to involve the dynamic interplay of several large-scale neural networks, including the fronto-parietal central ...executive network (CEN), cingulo-opercular salience network (SN), and the medial prefrontal-medial parietal default mode networks (DMN). It has been theorized that there is a causal neural mechanism by which the CEN/SN negatively regulate the DMN. Support for this idea has come from correlational neuroimaging studies; however, direct evidence for this neural mechanism is lacking. Here we undertook a direct test of this mechanism by combining transcranial magnetic stimulation (TMS) with functional MRI to causally excite or inhibit TMS-accessible prefrontal nodes within the CEN or SN and determine consequent effects on the DMN. Single-pulse excitatory stimulations delivered to only the CEN node induced negative DMN connectivity with the CEN and SN, consistent with the CEN/SN’s hypothesized negative regulation of the DMN. Conversely, low-frequency inhibitory repetitive TMS to the CEN node resulted in a shift of DMN signal from its normally low-frequency range to a higher frequency, suggesting disinhibition of DMN activity. Moreover, the CEN node exhibited this causal regulatory relationship primarily with the medial prefrontal portion of the DMN. These findings significantly advance our understanding of the causal mechanisms by which major brain networks normally coordinate information processing. Given that poorly regulated information processing is a hallmark of most neuropsychiatric disorders, these findings provide a foundation for ways to study network dysregulation and develop brain stimulation treatments for these disorders.
Background Major depressive disorder (MDD) has been shown to be associated with a disrupted topological organization of functional brain networks. However, little is known regarding whether these ...changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive whole-brain mapping of the white matter tracts that link regions distributed throughout the entire brain, the so-called human connectome. Methods We examined whole-brain structural networks in a cohort of 95 MDD outpatients and 102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity matrix representing probabilistic white matter connections between 84 parcellated cortical and subcortical regions using DTI tractography. Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization. Results Our network-based statistics analysis demonstrates lowered structural connectivity within two distinct brain networks that are present in depression: the first primarily involves the regions of the default mode network and the second comprises the frontal cortex, thalamus, and caudate regions that are central in emotional and cognitive processing. These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures. Conclusions This is the first report to use DTI to show the structural connectomic alterations present in MDD. Our findings highlight that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD.
The new field of 'precision psychiatry' Fernandes, Brisa S; Williams, Leanne M; Steiner, Johann ...
BMC medicine,
04/2017, Volume:
15, Issue:
1
Journal Article
Peer reviewed
Open access
Precision medicine is a new and important topic in psychiatry. Psychiatry has not yet benefited from the advanced diagnostic and therapeutic technologies that form an integral part of other clinical ...specialties. Thus, the vision of precision medicine as applied to psychiatry - 'precision psychiatry' - promises to be even more transformative than in other fields of medicine, which have already lessened the translational gap.
Herein, we describe 'precision psychiatry' and how its several implications promise to transform the psychiatric landscape. We pay particular attention to biomarkers and to how the development of new technologies now makes their discovery possible and timely. The adoption of the term 'precision psychiatry' will help propel the field, since the current term 'precision medicine', as applied to psychiatry, is impractical and does not appropriately distinguish the field. Naming the field 'precision psychiatry' will help establish a stronger, unique identity to what promises to be the most important area in psychiatry in years to come.
In summary, we provide a wide-angle lens overview of what this new field is, suggest how to propel the field forward, and provide a vision of the near future, with 'precision psychiatry' representing a paradigm shift that promises to change the landscape of how psychiatry is currently conceived.
Precision Psychiatry Williams, Leanne M; Hack, Laura M
2021, 20220101, 2021-10-15
eBook
Precision psychiatry, as outlined in this groundbreaking book, presents a new path forward. By integrating findings from basic and clinical neuroscience, clinical practice, and population-level data, ...the field seeks to develop therapeutic approaches tailored for specific individuals with a specific constellation of health issues, characteristics, strengths, and symptoms.
...if left untreated or insufficiently treated, they can rob individuals of the opportunity to live productive and satisfying lives. Building on the theme of childhood and adolescence, Leikauf ...discusses the clinical experience of introducing cognitive behavioral measures and models into a child psychiatry clinic focused on ADHD.7 Hack and Williams present a case illustration from an adult discovery clinic, in which functional neuroimaging, combined with cognitive-behavioral testing, life history, and symptom ratings, is used in a stratified approach to characterize the neurobehavioral profile of an individual patient and to inform the treatment strategy.8 In their perspective article, Bronstein et al. expand on novel computational approaches for precision psychiatry, highlighting an emerging class of machine-learning methods known as causal discovery analyses.9 These methods use data-driven insights to individualize case conceptualization and treatment selection. From the Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; and Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant ...treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.
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