A multi-site, multi-disorder resting-state magnetic resonance image database

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset

精神疾患の橋渡し研究のための機能的核磁気共鳴画像法に基づく手法開発

京都大学0048新制・課程博士博士(情報学)甲第21919号情博第702号新制||情||120(附属図書館)京都大学大学院情報学研究科システム科学専攻(主査)教授 石井 信, 教授 松田 哲也, 教授 加納 学, 川人 光男学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDGA

Brain state-based detection of attentional fluctuations and their modulation

In the search for brain markers of optimal attentional focus, the mainstream approach has been to first define attentional states based on behavioral performance, and to subsequently investigate “neural correlates” associated with these performance variations. However, this approach constrains the range of contexts in which attentional states can be operationalized by relying on overt behavior, and assumes a one-to-one correspondence between behavior and brain state. Here, we reversed the logic of these previous studies and sought to identify behaviorally-relevant brain states based solely on brain activity, agnostic to behavioral performance. In four independent datasets, we found that the same two brain states were dominant during a sustained attention task. One state was behaviorally optimal, with higher accuracy and stability, but a greater tendency to mind wander (State1). The second state was behaviorally suboptimal, with lower accuracy and instability (State2). We further demonstrate how these brain states were impacted by motivation and attention-deficit/hyperactivity disorder (ADHD). Individuals with ADHD spent more time in suboptimal State2 and less time in optimal State1 than healthy controls. Motivation overcame the suboptimal behavior associated with State2. Our study provides compelling evidence for the existence of two attentional states from the sole viewpoint of brain activity

Integration and segregation across large-scale intrinsic brain networks as a marker of sustained attention and task-unrelated thought

Sustained attention is a fundamental cognitive process that can be decoupled from distinct external events, and instead emerges from ongoing intrinsic large-scale network interdependencies fluctuating over seconds to minutes. Lapses of sustained attention are commonly associated with the subjective experience of mind wandering and task-unrelated thoughts. Little is known about how fluctuations in information processing underpin sustained attention, nor how mind wandering undermines this information processing. To overcome this, we used fMRI to investigate brain activity during subjects' performance (n=29) of a cognitive task that was optimized to detect and isolate continuous fluctuations in both sustained attention (via motor responses) and task-unrelated thought (via subjective reports). We then investigated sustained attention with respect to global attributes of communication throughout the functional architecture, i.e., by the segregation and integration of information processing across large scale-networks. Further, we determined how task-unrelated thoughts related to these global information processing markers of sustained attention. The results show that optimal states of sustained attention favor both enhanced segregation and reduced integration of information processing in several task-related large-scale cortical systems with concurrent reduced segregation and enhanced integration in the auditory and sensorimotor systems. Higher degree of mind wandering was associated with losses of the favored segregation and integration of specific subsystems in our sustained attention model. Taken together, we demonstrate that intrinsic ongoing neural fluctuations are characterized by two converging communication modes throughout the global functional architecture, which give rise to optimal and suboptimal attention states. We discuss how these results might potentially serve as neural markers for clinically abnormal attention. SIGNIFICANCE STATEMENT: Most of our brain activity unfolds in an intrinsic manner, i.e., is unrelated to immediate external stimuli or tasks. Here we use a gradual continuous performance task to map this intrinsic brain activity to both fluctuations of sustained attention and mind wandering. We show that optimal sustained attention is associated with concurrent segregation and integration of information processing within many large-scale brain networks, while task-unrelated thought is related to sub-optimal information processing in specific subsystems of this sustained attention network model. These findings provide a novel information processing framework for investigating the neural basis of sustained attention, by mapping attentional fluctuations to genuinely global features of intra-brain communication

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability