William R. Shirer

Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns

Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.

Gender modulates the APOE ε4 effect in healthy older adults: convergent evidence from functional brain connectivity and spinal fluid tau levels.

We examined whether the effect of the apolipoprotein E (APOE) genotype on functional brain connectivity is modulated by gender in healthy older human adults. Our results confirm significantly decreased connectivity in the default mode network in healthy older APOE ε4 carriers compared with ε3 homozygotes. More important, further testing revealed a significant interaction between APOE genotype and gender in the precuneus, a major default mode hub. Female ε4 carriers showed significantly reduced default mode connectivity compared with either female ε3 homozygotes or male ε4 carriers, whereas male ε4 carriers differed minimally from male ε3 homozygotes. An additional analysis in an independent sample of healthy elderly using an independent marker of Alzheimers disease, i.e., spinal fluid levels of tau, provided corresponding evidence for this gender-by-APOE interaction. Together, these results converge with previous work showing a higher prevalence of the ε4 allele among women with Alzheimers disease and, critically, demonstrate that this interaction between APOE genotype and gender is detectable in the preclinical period.

Keeping Your Eyes on the Prize: Anger and Visual Attention to Threats and Rewards

People’s emotional states influence what they focus their attention on in their environment. For example, fear focuses people’s attention on threats, whereas excitement may focus their attention on rewards. This study examined the effect of anger on overt visual attention to threats and rewards. Anger is an unpleasant emotion associated with approach motivation. If the effect of emotion on visual attention depends on valence, we would expect anger to focus people’s attention on threats. If, however, the effect of emotion on visual attention depends on motivation, we would expect anger to focus people’s attention on rewards. Using an eye tracker, we examined the effects of anger, fear, excitement, and a neutral emotional state on participants’ overt visual attention to threatening, rewarding, and control images. We found that anger increased visual attention to rewarding information, but not to threatening information. These findings demonstrate that anger increases attention to potential rewards and suggest that the effects of emotions on visual attention are motivationally driven.

Disentangling Dynamic Networks: Separated and Joint Expressions of Functional Connectivity Patterns in Time

Resting‐state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k‐means clustering and sliding‐window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task‐based, and resting‐state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject‐driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting‐state dFC. Hum Brain Mapp 35:5984–5995, 2014.

Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination.

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly important tool in mapping the functional networks of the brain. This tool has been used to examine network changes induced by cognitive and emotional states, neurological traits, and neuropsychiatric disorders. However, noise that remains in the rs-fMRI data after preprocessing has limited the reliability of individual-subject results, wherein scanner artifacts, subject movements, and other noise sources induce non-neural temporal correlations in the blood oxygen level-dependent (BOLD) timeseries. Numerous preprocessing methods have been proposed to isolate and remove these confounds; however, the field has not coalesced around a standard preprocessing pipeline. In comparisons, these preprocessing methods are often assessed with only a single metric of rs-fMRI data quality, such as reliability, without considering other aspects in tandem, such as signal-to-noise ratio and group discriminability. The present study seeks to identify the data preprocessing pipeline that optimizes rs-fMRI data across multiple outcome measures. Specifically, we aim to minimize the noise in the data and maximize result reliability, while retaining the unique features that characterize distinct groups. We examine how these metrics are influenced by bandpass filter selection and noise regression in four datasets, totaling 181 rs-fMRI scans and 38 subject-driven memory scans. Additionally, we perform two different rs-fMRI analysis methods - dual regression and region-of-interest based functional connectivity - and highlight the preprocessing parameters that optimize both approaches. Our results expand upon previous reports of individual-scan reliability, and demonstrate that preprocessing parameter selection can significantly change the noisiness, reliability, and heterogeneity of rs-fMRI data. The application of our findings to rs-fMRI data analysis should improve the validity and reliability of rs-fMRI results, both at the individual-subject level and the group level.

Intrinsic and task-dependent coupling of neuronal population activity in human parietal cortex.

Human neuroimaging studies have suggested that subregions of the medial and lateral parietal cortex form key nodes of a larger brain network supporting episodic memory retrieval. To explore the electrophysiological correlates of functional connectivity between these subregions, we recorded simultaneously from medial and lateral parietal cortex using intracranial electrodes in three human subjects. We observed electrophysiological co-activation of retrosplenial/posterior cingulate cortex (RSC/PCC) and angular gyrus (AG) in the high-frequency broadband (HFB, or high-gamma) range, for conditions that required episodic retrieval. During resting and sleeping states, slow fluctuations (<1 Hz) of HFB activity were highly correlated between these task-co-activated neuronal populations. Furthermore, intrinsic electrophysiological connectivity patterns matched those obtained with resting-state fMRI from the same subjects. Our findings quantify the spatiotemporal dynamics of parietal cortex during episodic memory retrieval and provide clear neurophysiological correlates of intrinsic and task-dependent functional connectivity in the human brain.

The posterior medial cortex in urologic chronic pelvic pain syndrome: detachment from default mode network-a resting-state study from the MAPP Research Network.

Abstract Altered resting-state (RS) brain activity, as a measure of functional connectivity (FC), is commonly observed in chronic pain. Identifying a reliable signature pattern of altered RS activity for chronic pain could provide strong mechanistic insights and serve as a highly beneficial neuroimaging-based diagnostic tool. We collected and analyzed RS functional magnetic resonance imaging data from female patients with urologic chronic pelvic pain syndrome (N = 45) and matched healthy participants (N = 45) as part of an NIDDK-funded multicenter project (www.mappnetwork.org). Using dual regression and seed-based analyses, we observed significantly decreased FC of the default mode network to 2 regions in the posterior medial cortex (PMC): the posterior cingulate cortex (PCC) and the left precuneus (threshold-free cluster enhancement, family-wise error corrected P < 0.05). Further investigation revealed that patients demonstrated increased FC between the PCC and several brain regions implicated in pain, sensory, motor, and emotion regulation processes (eg, insular cortex, dorsolateral prefrontal cortex, thalamus, globus pallidus, putamen, amygdala, hippocampus). The left precuneus demonstrated decreased FC to several regions of pain processing, reward, and higher executive functioning within the prefrontal (orbitofrontal, anterior cingulate, ventromedial prefrontal) and parietal cortices (angular gyrus, superior and inferior parietal lobules). The altered PMC connectivity was associated with several phenotype measures, including pain and urologic symptom intensity, depression, anxiety, quality of relationships, and self-esteem levels in patients. Collectively, these findings indicate that in patients with urologic chronic pelvic pain syndrome, regions of the PMC are detached from the default mode network, whereas neurological processes of self-referential thought and introspection may be joined to pain and emotion regulatory processes.

Disordered reward processing and functional connectivity in trichotillomania: A pilot study

BACKGROUND The neurobiology of Trichotillomania is poorly understood, although there is increasing evidence to suggest that TTM may involve alterations of reward processing. The current study represents the first exploration of reward processing in TTM and the first resting state fMRI study in TTM. We incorporate both event-related fMRI using a monetary incentive delay (MID) task, and resting state fMRI, using two complementary resting state analysis methodologies (functional connectivity to the nucleus accumbens and dual regression within a reward network) in a pilot study to investigate differences in reward processing between TTM and healthy controls (HC). METHODS 21 unmedicated subjects with TTM and 14 HC subjects underwent resting state fMRI scans. A subset (13 TTM and 12 HC) also performed the MID task. RESULTS For the MID task, TTM subjects showed relatively decreased nucleus accumbens (NAcc) activation to reward anticipation, but relative over-activity of the NAcc to both gain and loss outcomes. Resting state functional connectivity analysis showed decreased connectivity of the dorsal anterior cingulate (dACC) to the NAcc in TTM. Dual regression analysis of a reward network identified through independent component analysis (ICA) also showed decreased dACC connectivity and more prominently decreased basolateral amygdala connectivity within the reward network in TTM. CONCLUSIONS Disordered reward processing at the level of NAcc, also involving decreased modulatory input from the dACC and the basolateral amygdala may play a role in the pathophysiology of TTM.

Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm

Rumination, an internal cognitive state characterized by recursive thinking of current self-distress and past negative events, has been found to correlate with the development of depressive disorders. Here, we investigated the feasibility of using connectivity for distinguishing different emotional states induced by a novel free-streaming, subject-driven experimental paradigm. Connectivity between 78 functional regions of interest (ROIs) within 14 large-scale networks and 6 structural ROIs particularly relevant to emotional processing were used for classifying 4 mental states in 19 healthy controls. The 4 mental states comprised: An unconstrained period of mind wandering; a ruminative mental state self-induced by recalling a time of personal disappointment; a euphoric mental state self-induced by recalling what brings the subject joy; and a sequential episodic recollection of the events of the day. A support vector machine achieved accuracies ranging from 89% to 94% in classifying pairs of different mental states. We reported the most significant brain connections that best discriminated these mental states. In particular, connectivity changes involving the amygdala were found to be important for distinguishing the rumination condition from the other mental states. Our results demonstrated that connectivity-based classification of subject-driven emotional states constitutes a novel and effective approach for studying ruminative behavior.