Biology subjects

Farb, N. A.

Publications and source records attributed to Farb, N. A..

3 recordsLinked to original sources

bioRxiv2022

A Machine Learning Approach Towards the Differentiation Between Interoceptive and Exteroceptive Attention

Interoception, the representation of the bodys internal state, plays a central role in emotion, motivation, and wellbeing. Interoceptive attention is qualitatively different from attention to the external senses and may recruit a distinct neural system, but the neural separability of interoceptive and exteroceptive attention is unclear. We used a machine learning approach to classify neural correlates of interoceptive and exteroceptive attention in a randomized control trial of interoceptive training (MABT). Participants in the training and control groups attended fMRI assessment before and after an 8-week intervention period (N = 44 scans). The imaging paradigm manipulated attention targets (breath vs. visual stimulus) and reporting demands (active reporting vs. passive monitoring). Machine learning models achieved high accuracy in distinguishing between interoceptive and exteroceptive attention using both in-sample and more stringent out-of-sample tests. We then explored the potential of these classifiers in "reading out" mental states in a sustained interoceptive attention task. Participants were classified as maintaining an active reporting state for only [~]90s of each 3-minute sustained attention period. Within this active period, interoceptive training enhanced participants ability to sustain interoceptive attention. These findings demonstrate that interoceptive and exteroceptive attention engage reliable and distinct neural networks; machine learning classifiers trained on this distinction show promise for assessing the stability of interoceptive attention, with implications for the future assessment of mental health and treatment response.

neuroscience↗

bioRxiv2022

Neural Dynamics of Interoceptive Attention and Awareness: A Within-Participant fMRI Study

Interoception, the representation of the bodys internal state, serves as a foundation for emotion, motivation, and wellbeing. Yet despite its centrality in human experience, the neural mechanisms of interoception are poorly understood. The Interoceptive/Exteroceptive Attention Task (IEAT) is a novel neuroimaging paradigm that compares behavioral tracking of the respiratory cycle (Active Interoception) to tracking of a visual stimulus (Active Exteroception). Twenty-two healthy participants completed the IEAT during two separate scanning sessions (N = 44) as part of a randomized control trial of Mindful Awareness in Body-oriented Therapy (MABT). Compared to Exteroception, Interoception deactivated somatomotor and prefrontal regions. Greater interoceptive sensibility (MAIA scale) predicted sparing from deactivation along the anterior cingulate cortex (ACC) and left-lateralized language regions. The right insula--typically described as a primary interoceptive cortex--was only implicated by its further deactivation during an exogenously paced respiration condition (Active Matching). Psychophysiological interaction analysis characterized Active Interoception as promoting greater ACC connectivity with lateral frontal and parietal regions commonly referred to as the Dorsal Attention Network. Interoception of the breath may therefore involve reduced cortical activity but greater connectivity, with greater sensibility sparing cortical inhibition within well-characterized attentional networks. In contrast to a literature that relates detection of liminal signals such as the heartbeat to anterior insula activity, attention towards accessible body sensations such as the breath may lead to a low activity, high connectivity state in which sensory signals from the body may be better discerned. Significance StatementInteroception, the representation of the bodys internal state, is poorly understood compared to the external senses, with existing neuroimaging studies failing to match task difficulty between interoceptive and exteroceptive tasks. The present study used a novel fMRI task to compare interoceptive and exteroceptive attention, and how this distinction was moderated by self-reported interoceptive awareness. The results implicate three novel interoceptive mechanisms: interoception reduces cortical activity while increasing connectivity, wherein awareness is linked to preserved activation of the brains salience network and left-lateralized language regions. These findings characterize interoception as a lower activity state in which awareness depends upon the ability to notice and report on body signals typically obscured by the processing of exteroceptive information and other forms of cognition.

neuroscience↗

bioRxiv2018

Focus on the breath: Brain decoding reveals internal states of attention during meditation

Meditation practices are used to cultivate internally-oriented attention to bodily sensations, which may improve health via cognitive and emotion regulation of bodily signals. However, it remains unclear how meditation impacts internal attention states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to (1) learn and recognize internal attentional (IA) states relevant for meditation during a directed IA task, and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N=16), we first used MVPA to develop single-subject brain classifiers for 5 modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states (i.e., meditation-related states: breath attention, mind wandering, and self-referential processing, and control states: attention to feet and sounds). Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all block volumes were tested, N=2160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were then applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, mind wandering, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to mind wandering or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.

neuroscience↗