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1. Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia

   https://doi.org/10.1161/STROKEAHA.121.036749  

   Billot, Anne; Lai, Sha; Varkanitsa, Maria; Braun, Emily J.; Rapp, Brenda; Parrish, Todd B.; Higgins, James; Kurani, Ajay S.; Caplan, David; Thompson, Cynthia K.; et al (May 2022, Stroke)

   Background: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. Methods: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. Results: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P <0.001) or a single feature set (F1 range=0.68–0.84, P <0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). Conclusions: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors. 

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2. he Brain and Language in Autism In: Language in Autism, edited by

   Larson, C; Eigsti, IM (August 2025, Wiley Blackwell)

   Schaeffer, J; Novogrodsky, R; Perovic, A; Prévost, P; Tuller, L (Ed.)

   Alongside the behavioral features of autism, this neurodevelopmental disorder is characterized by important differences in the neural circuitry underlying language processing. Regarding brain structure, most neurotypical individuals have larger left-hemisphere volumes of brain regions that are important for language, compared to the same regions in the right hemisphere (the right half of the brain). This asymmetry is due to neural specialization of left hemisphere regions for the purpose of language functions. In contrast, the brains of autistic individuals seem to be more symmetrical, suggesting that language difficulties are associated with reduced left hemisphere specialization for language in the brain. The activity of brain regions involved in language also differs in autism. Examining brain activity reveals nuanced and important differences in the processes underlying language production and comprehension in neurotypical and autistic individuals, even when their language behavior appears similar. 

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3. Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling

   https://doi.org/10.1162/imag_a_00488  

   Blauch, Nicholas_M; Plaut, David_C; Vin, Raina; Behrmann, Marlene (March 2025, Imaging Neuroscience)

   Abstract The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in high-level vision. One intriguing aspect of this region is its functional lateralization, with neural responses to words being stronger in the left hemisphere, and neural responses to faces being stronger in the right hemisphere; such patterns can be summarized with a signed laterality index (LI), positive for leftward laterality. Converging evidence has suggested that word laterality emerges to couple efficiently with left-lateralized frontotemporal language regions, but evidence is more mixed regarding the sources of the right lateralization for face perception. Here, we use individual differences as a tool to test three theories of VTC organization arising from (1) local competition between words and faces driven by long-range coupling between words and language processes, (2) local competition between faces and other categories, and (3) long-range coupling with VTC and temporal areas exhibiting local competition between language and social processing. First, in an in-house functional MRI experiment, we did not obtain a negative correlation in the LIs of word and face selectivity relative to object responses, but did find a positive correlation when using selectivity relative to a fixation baseline, challenging ideas of local competition between words and faces driving rightward face lateralization. We next examined broader local LI interactions with faces using the large-scale Human Connectome Project (HCP) dataset. Face and tool LIs were significantly anti-correlated, while face and body LIs were positively correlated, consistent with the idea that generic local representational competition and cooperation may shape face lateralization. Last, we assessed the role of long-range coupling in the development of VTC lateralization. Within our in-house experiment, substantial positive correlation was evident between VTC text LI and that of several other nodes of a distributed text-processing circuit. In the HCP data, VTC face LI was both negatively correlated with language LI and positively correlated with social processing in different subregions of the posterior temporal lobe (PSL and STSp, respectively). In summary, we find no evidence of local face–word competition in VTC; instead, more generic local interactions shape multiple lateralities within VTC, including face laterality. Moreover, face laterality is also influenced by long-range coupling with social processing in the posterior temporal lobe, where social processing may become right lateralized due to local competition with language. 

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4. Assessing the impact of context inference error and partial observability on RL methods for just-in-time adaptive interventions

   Karine, Karine; Klasnja, Predrag; Murphy, Susan A.; Marlin, Benjamin M (July 2023, UAI '23: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)

   Evans, Robin J.; Shpitser, Ilya (Ed.)

   Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information. 

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5. Chronic Stress Decreases Lactation Performance

   https://doi.org/10.1093/icb/icad044  

   Josefson, Chloe_C; De Moura Pereira, Lucelia; Skibiel, Amy_L (May 2023, Integrative And Comparative Biology)

   Synopsis The ability to provision offspring with milk is a significant adaptive feature of mammals that allows for considerable maternal regulation of offspring beyond gestation, as milk provides complete nutrition for developing neonates. For mothers, lactation is a period of marked increases in energetic and nutritive demands to support milk synthesis; because of this considerable increase in demand imposed on multiple physiological systems, lactation is particularly susceptible to the effects of chronic stress. Here, we present work that explores the impact of chronic stress during lactation on maternal lactation performance (i.e., milk quality and quantity) and the expression of key milk synthesis genes in mammary tissue using a Sprague–Dawley rat model. We induced chronic stress using a well-established, ethologically relevant novel male intruder paradigm for 10 consecutive days during the postpartum period. We hypothesized that the increased energetic burden of mounting a chronic stress response during lactation would decrease lactation performance. Specifically, we predicted that chronic exposure to this social stressor would decrease either milk quality (i.e., composition of proximate components and energy density) or quantity. We also predicted that changes in proximate composition (i.e., lipid, lactose, and protein concentrations) would be associated with changes in gene expression levels of milk synthesis genes. Our results supported our hypothesis that chronic stress impairs lactation performance. Relative to the controls, chronically stressed rats had lower milk yields. We also found that milk quality was decreased; milk from chronically stressed mothers had lower lipid concentration and lower energy density, though protein and lactose concentrations were not different between treatment groups. Although there was a change in proximate composition, chronic stress did not impact mammary gland expression of key milk synthesis genes. Together, this work demonstrates that exposure to a chronic stressor impacts lactation performance, which in turn has the potential to impact offspring development via maternal effects. 

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