Data-driven tensor independent component analysis for model-based connectivity neurofeedback

Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA

Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.

Feasibility of real-time fMRI neurofeedback for rehabilitation of reading deficits in aphasia

Background

Reading impairments, a common consequence of stroke-induced aphasia, significantly hinder life participation, affecting both functional and leisure activities. Traditional post-stroke rehabilitation strategies often show limited generalization beyond trained materials, underscoring the need for novel interventions targeting the underlying neural mechanisms.

Method

This study investigates the feasibility and potential effectiveness of real-time functional magnetic resonance imaging (fMRI) neurofeedback (NFB) intervention for reading deficits associated with stroke and aphasia. We enrolled left-hemisphere stroke survivors in the subacute recovery period and healthy controls in an fMRI NFB intervention study focusing on increasing activation within the left supramarginal gyrus (SMG), a critical region for reading supporting orthography-phonology conversion.

Results

Preliminary findings demonstrate that stroke participants showed significant improvements in reading comprehension and phonological awareness, as evidenced by marked gains on the Reading Comprehension Battery for Aphasia (RCBA) and a phonology two-alternative forced choice test. Functional MRI results indicated that stroke participants exhibited increased activation from day 1 to day 3 of NFB training within the left SMG and the broader left hemisphere reading network, particularly during challenging nonword reading tasks. Healthy controls also showed increased activation during NFB regulation and reading tasks, but these changes were outside the traditional reading network, involving regions associated with cognitive control, reward anticipation, and learning. In both stroke participants and healthy controls, we also found changes in dynamic functional connectivity of multiple resting state networks from before to after NFB training.

Conclusions

Although preliminary, this research contributes to the development of biologically informed interventions for reading deficits in aphasia, representing an early step towards improving post-stroke rehabilitation outcomes. Future randomized controlled trials are necessary to validate these findings by including a sham NFB control group within a larger participant sample.

Registration

The study was preregistered on ClinicalTrials.gov, NCT# NCT04875936.

Real-Time Functional MRI in the Treatment of Mental Health Disorders

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks

Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.

Data-Driven Approach to the Analysis of Real-Time FMRI Neurofeedback Data: Disorder-Specific Brain Synchrony in PTSD

Brain-computer interfaces (BCIs) can be used in real-time fMRI neurofeedback (rtfMRI NF) investigations to provide feedback on brain activity to enable voluntary regulation of the blood-oxygen-level dependent (BOLD) signal from localized brain regions. However, the temporal pattern of successful self-regulation is dynamic and complex. In particular, the general linear model (GLM) assumes fixed temporal model functions and misses other dynamics. We propose a novel data-driven analyses approach for rtfMRI NF using intersubject covariance (ISC) analysis. The potential of ISC was examined in a reanalysis of data from 21 healthy individuals and nine patients with post-traumatic stress-disorder (PTSD) performing up-regulation of the anterior cingulate cortex (ACC). ISC in the PTSD group differed from healthy controls in a network including the right inferior frontal gyrus (IFG). In both cohorts, ISC decreased throughout the experiment indicating the development of individual regulation strategies. ISC analyses are a promising approach to reveal novel information on the mechanisms involved in voluntary self-regulation of brain signals and thus extend the results from GLM-based methods. ISC enables a novel set of research questions that can guide future neurofeedback and neuroimaging investigations.

Metabolic underpinnings of activated and deactivated cortical areas in human brain

Neuroimaging with functional MRI (fMRI) identifies activated and deactivated brain regions in task-based paradigms. These patterns of (de)activation are altered in diseases, motivating research to understand their underlying biochemical/biophysical mechanisms. Essentially, it remains unknown how aerobic metabolism of glucose to lactate (aerobic glycolysis) and excitatory-inhibitory balance of glutamatergic and GABAergic neuronal activities vary in these areas. In healthy volunteers, we investigated metabolic distinctions of activating visual cortex (VC, a task-positive area) using a visual task and deactivating posterior cingulate cortex (PCC, a task-negative area) using a cognitive task. We used fMRI-guided J-edited functional MRS (fMRS) to measure lactate, glutamate plus glutamine (Glx) and γ-aminobutyric acid (GABA), as indicators of aerobic glycolysis and excitatory-inhibitory balance, respectively. Both lactate and Glx increased upon activating VC, but did not change upon deactivating PCC. Basal GABA was negatively correlated with BOLD responses in both brain areas, but during functional tasks GABA decreased in VC upon activation and GABA increased in PCC upon deactivation, suggesting BOLD responses in relation to baseline are impacted oppositely by task-induced inhibition. In summary, opposite relations between BOLD response and GABAergic inhibition, and increases in aerobic glycolysis and glutamatergic activity distinguish the BOLD response in (de)activated areas.

Brain networks for engaging oneself in positive-social emotion regulation

Positive emotions facilitate cognitive performance, and their absence is associated with burdening psychiatric disorders. However, the brain networks regulating positive emotions are not well understood, especially with regard to engaging oneself in positive-social situations. Here we report convergent evidence from a multimodal approach that includes functional magnetic resonance imaging (fMRI) brain activations, meta-analytic functional characterization, Bayesian model-driven analysis of effective brain connectivity, and personality questionnaires to identify the brain networks mediating the cognitive up-regulation of positive-social emotions. Our comprehensive approach revealed that engaging in positive-social emotion regulation with a self-referential first-person perspective is characterized by dynamic interactions between functionally specialized prefrontal cortex (PFC) areas, the temporoparietal junction (TPJ) and the amygdala. Increased top-down connectivity from the superior frontal gyrus (SFG) controls affective valuation in the ventromedial and dorsomedial PFC, self-referential processes in the TPJ, and modulate emotional responses in the amygdala via the ventromedial PFC. Understanding the brain networks engaged in the regulation of positive-social emotions that involve a first-person perspective is important as they are known to constitute an effective strategy in therapeutic settings.