Data_Sheet_1_Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder.pdf

Functional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neuroimaging technique in research of cognitive neurosciences and of neural mechanisms of neuropsychiatric/neurological diseases. A primary goal of fMRI-based neuroimaging studies is to identify biomarkers for brain-behavior relationship and ultimately perform individualized treatment outcome prognosis. However, the concern of inadequate validation and the nature of small sample sizes are associated with fMRI-based neuroimaging studies, both of which hinder the translation from scientific findings to clinical practice. Therefore, the current paper presents a modeling approach to predict time-dependent prognosis with fMRI-based brain metrics and follow-up data. This prediction modeling is a combination of seed-based functional connectivity and voxel-wise Cox regression analysis with built-in nested cross-validation, which has been demonstrated to be able to provide robust and unbiased model performance estimates. Demonstrated with a cohort of treatment-seeking cocaine users from psychosocial treatment programs with 6-month follow-up, our proposed modeling method is capable of identifying brain regions and related functional circuits that are predictive of certain follow-up behavior, which could provide mechanistic understanding of neuropsychiatric/neurological disease and clearly shows neuromodulation implications and can be used for individualized prognosis and treatment protocol design.</p

Data_Sheet_1_Low frequency repetitive transcranial magnetic stimulation to the right dorsolateral prefrontal cortex engages thalamus, striatum, and the default mode network.PDF

The positive treatment outcomes of low frequency (LF) repetitive transcranial magnetic stimulation (rTMS) when applied over the right dorsolateral prefrontal cortex (DLPFC) in treatment-refractory depression has been verified. However, the mechanism of action behind these results have not been well-explored. In this work we used simultaneous functional magnetic resonance imaging (fMRI) during TMS to explore the effect of LF rTMS on brain activity when applied to the right [RDLPFC1 (MNI: 50, 30, 36)] and left DLPFC sites [LDLPFC1 (MNI: -50, 30, 36), LDLPFC2 (MNI: -41, 16, 54)]. Seventeen healthy adult volunteers participated in this study. To identify brain areas affected by rTMS, an independent component analysis and a general linear model were used. Our results showed an important laterality effect when contrasting rTMS over the left and right sites. Specifically, LF rTMS increased brain activity at the striatum, thalamus, and areas of the default mode network when applied to the right, but not to the contralateral left DLPFC. In contrast, no site differences were observed when evaluating the effect of LF rTMS over the two left sites. These findings demonstrate that LF rTMS to the right DLPFC was able to stimulate the cortico-striato-thalamo-cortical pathway, which is dysregulated in patients with major depressive disorder; therefore, possibly providing some neurobiological justification for the successful outcomes found thus far for LF rTMS in the treatment of depression.</p

Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity

<div><p>Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation.</p></div

Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity - Fig 3

<p>(a) Significant increase in sleepiness score after 36 h of TSD (paired t-test, p = 0.00011). (b) Regression plots of correlation between the salience network (SN) and the default mode network (DMN) against sleepiness score. *p < 0.01, statistical significance.</p

Significant increase in correlation between the salience network (SN) and the default mode network (DMN) (paired t-test, p = 0.0012) in the TSD vs RW states.

<p>LECN indicates left executive control network; RECN, right executive control network. *p < 0.01, statistical significance.</p

Activation results from single-group, whole-brain, voxel-wise analysis of centromedial amygdale.

<p>Activation results from single-group, whole-brain, voxel-wise analysis of centromedial amygdale.</p

Whole-brain functional connectivity patterns of the BLA and CMA before and after 36-h TSD.

<p>Brain regions with positive correlations are displayed in warm colors, while negative correlations are displayed in cool colors.</p

Brain areas that exhibited altered functional connectivity after 36 h TSD.

<p>The left sides of the images in transverse views represent the right hemisphere.</p

Behavioral data in RW state and after 36 h of TSD.

<p>Behavioral data in RW state and after 36 h of TSD.</p

Group-level components revealed by the temporal concatenation group independent component analysis (ICA).

<p>Four networks generated from group ICA of the resting state data were identified as the default mode network (DMN), left and right executive control network (ECN) and salience network (SN). Spatial maps were converted to z score images and then thresholded at z = 3.5 via mixture model fit. Network maps are displayed in red-yellow overlaid onto the Talairach space based on radiological convention (left = right).</p