Can brain state be manipulated to emphasize individual differences in functional connectivity?

Finn, Emily S.; Mayes, Linda C.; Finn, Daniel M.; Shen, Xilin; Papademetris, Xenophon; Constable, R. Todd

doi:10.1016/j.neuroimage.2017.03.064

Cited by 281 publications

(284 citation statements)

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“…The classification indicated fair performance with 73% accuracy, and 75% AUC. To improve their performance, future studies should adopt an individualized functional atlas rather than group parcellation for cross‐subject alignment (Wang et al, ), and a movement‐related rather than resting‐state design (Finn et al, ). Second, transcranial magnetic stimulation (TMS) is an noninvasive neuro‐modulation technique, which has shown potential to treat PD (Chou, Hickey, Sundman, Song, & Chen, ); however, its mechanism is still largely unknown.…”

Section: Discussionmentioning

confidence: 99%

Regional and network properties of white matter function in Parkinson's disease

Cuiping

Liu

et al. 2018

Human Brain Mapping

100

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Parkinson's disease (PD) is a neurodegenerative disorder with dysfunction in cortices as well as white matter (WM) tracts. While the changes to WM structure have been extensively investigated in PD, the nature of the functional changes to WM remains unknown. In this study, the regional activity and functional connectivity of WM were compared between PD patients (n = 57) and matched healthy controls (n = 52), based on multimodel magnetic resonance imaging data sets. By tract‐based spatial statistical analyses of regional activity, patients showed decreased structural‐functional coupling in the left corticospinal tract compared to controls. This tract also displayed abnormally increased functional connectivity within the left post‐central gyrus and left putamen in PD patients. At the network level, the WM functional network showed small‐worldness in both controls and PD patients, yet it was abnormally increased in the latter group. Based on the features of the WM functional connectome, previously un‐evaluated individuals could be classified with fair accuracy (73%) and area under the curve of the receiver operating characteristics (75%). These neuroimaging findings provide direct evidence for WM functional changes in PD, which is crucial to understand the functional role of fiber tracts in the pathology of neural circuits.

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Section: Discussionmentioning

confidence: 99%

Regional and network properties of white matter function in Parkinson's disease

Cuiping

Liu

et al. 2018

Human Brain Mapping

100

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“…Primarily based on group‐level analysis, we have accumulated abundant knowledge about the universal principles of brain function (Raichle, ). However, it is still undetermined how these advances can be generalized to solve individualized problems, such as clinical diagnosis (Arbabshirani, Plis, Sui, & Calhoun, ; Finn et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…generalized to solve individualized problems, such as clinical diagnosis (Arbabshirani, Plis, Sui, & Calhoun, 2017;Finn et al, 2017).…”

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confidence: 99%

Classification of schizophrenia by intersubject correlation in functional connectome

Chen

Bai

et al. 2019

Human Brain Mapping

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Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting‐state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH–SCH) than different groups (i.e., HC–SCH). These findings suggest that the average connectome within group (termed as group‐specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75–77%) and area under curve (81–86%) were found in discriminating SCH from HC or depression patients by GFC‐based leave‐one‐out cross‐validation. Cross‐center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group‐specific biological features and may be helpful in clinical diagnosis for schizophrenia.

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“…Thus, the idea of examining fcMRI during task states as a way mitigate subject demand in psychopathology populations is an intriguing one, but more work will be needed to determine optimal processing streams (e.g., whether to remove task design signals) and how much aggregation across multiple tasks is needed to best approximate rsfcMRI. Further, it will be important to determine whether or not differences as a function of psychopathology or relationships to individual differences in behavior are equally apparent in either rsfcMRI or task aggregated fcMRI, which could be true even if there are mean level differences in fcMRI across states 97 .…”

Section: Advances In Our Understanding Of the Nature Of Brain Networkmentioning

confidence: 99%

Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology

Deanna

2017

Harv Rev Psychiatry

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A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts in the field to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior, which in turns provides a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. The focus of this review will be on the use of resting state functional connectivity (rsfcMRI) to assess brain circuits, the advances in this field generated by the Human Connectome Project, and the relevance of these advances for understanding neural circuit dysfunction in psychopathology. I focus on the methods developed by the Human Connectome Project that may be particularly relevant to studies of psychopathology, outline some of the key findings in terms of what constitutes a brain region, and highlight new information about the nature and stability of brain circuits. I then describe some of the new findings from the Human Connectome Project about neural circuits and their relationships to behavior that may be particularly relevant to psychopathology. Finally, I end on future directions in terms of extensions of the Human Connectome Project methods across the lifespan and into manifest illness, as well as a discussion of potential treatment implications.

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Can brain state be manipulated to emphasize individual differences in functional connectivity?

Cited by 281 publications

References 100 publications

Regional and network properties of white matter function in Parkinson's disease

Regional and network properties of white matter function in Parkinson's disease

Classification of schizophrenia by intersubject correlation in functional connectome

Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology

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