Joy Hirsch

Background: While social interaction difficulties are common in schizophrenia, the neural underpinnings for its social symptomatology are under-explored. In this study, we aimed to isolate atypical neural systems associated with live social interaction in first episode psychosis (early schizophrenia spectrum disorder). Methods: A two-person live face gaze paradigm was employed as the behavioral proxy for social interaction while electroencephalography (EEG), functional near infrared spectroscopy (fNIRS), eye tracking, and facial movement measures were simultaneously acquired. Findings were compared for 24 first episode psychosis (FEP) patients and 32 typically developed (TD) participants to test the hypothesis that these neural and behavioral measures would isolate atypical social neural systems in the FEP group. Results: FNIRS neural data showed atypical hemodynamic activity in the right temporoparietal region, known to be associated with live face-to-face interactions. This atypical activity was significantly correlated with expressive deficit score, a sub score derived from PANSS negative symptom domain and N4, a PANSS negative symptom which measures passive/apathetic social withdrawal. Involuntary behavioral responses associated with spontaneous facial mimicry and gaze fixation were reduced in FEP. Consistent with previous findings, Event Related Potential (ERP) amplitudes and latencies were atypical in FEP during the first 100–300 ms in the right temporoparietal electrodes, along with reduced theta oscillation power. Conclusion: Together, the identified regions represent atypical live face processing systems in early schizophrenia spectrum disorder, which is further correlated with expressive deficits and social withdrawal. The study emphasizes the need for ecologically valid multimodal data acquisition to detect neural hubs crucial in live face processing in early schizophrenia.

SignificanceRepetition-based learning supports word recognition and reading fluency, yet its neural mechanisms remain poorly understood, particularly where deficits in consolidating repeated linguistic input may underlie reading and language difficulties. Establishing reliable neural markers of repetition-based learning is a critical first step toward characterizing the phenotypes of these difficulties in children.AimWe developed a whole-brain simultaneous functional near-infrared spectroscopy–electroencephalography (fNIRS–EEG) approach to capture fast temporal electrophysiological and spatially accurate hemodynamic signals during long-lag repetition learning.ApproachThirty participants performed a semantic judgment task with words repeated across four runs with intervening items, modeling repetition demands central to reading instruction. A multimodal decoding framework integrating electrophysiological, hemodynamic, and cross-modal fNIRS–EEG correlation features was used to classify repetition-based learning.ResultsRepeated words showed response time reductions. EEG captured a frontal N400-range repetition-related negativity and attenuation of alpha–beta suppression, reflecting changes in recognition-related and post-recognition processing. fNIRS revealed left inferior frontal and superior temporal efficiency increases alongside right supramarginal activation, suggesting integrative processing. Multimodal classifiers outperformed unimodal classifiers, indicating that each modality captured complementary aspects of learning.ConclusionsCombining fNIRS and EEG with multimodal machine learning provides a richer characterization of repetition-based learning than either modality alone, establishing a framework for phenotyping language learning capacity and identifying neural markers of reading difficulties in pediatric populations.

Social anxiety disorder (SAD) is among the most prevalent anxiety disorders, and it has been associated with signs of advanced biological ageing. Despite this, brain age research on anxiety disorders remains limited. This mega-analysis investigated brain ageing in adults with SAD within the ENIGMA-Anxiety Working Group. Structural MRI scans from 576 participants with SAD and 1 355 non-affected healthy controls (HCs) across 26 international samples were included. Brain age was estimated from 77 cortical and subcortical regions using a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as the difference between brain age and chronological age. Group and subgroup differences (comorbidity, medication) were assessed using linear mixed-effect models. In the full sample, there was no group difference in brain-PAD ( β diagnosis (SE)=0.70 (0.37) years, p =0.061). In a subgroup of participants with SAD with comorbid anxiety disorders (n=184 SAD, n=1 355 HCs), a brain-PAD of +2.39 (0.93) years (Cohen’s d =0.23, p FDR=0.003) was observed. This brain-PAD became smaller after exclusion of participants with comorbid agoraphobia and specific phobia, suggesting that these disorders may partly drive the advanced brain-PAD. In conclusion, this ENIGMA-Anxiety mega-analysis did not find evidence of advanced brain ageing in the full sample of adult participants with SAD relative to HCs. However, a sub-analysis suggested that SAD with co-occurring phobic disorders, or the phobic disorders themselves, are associated with neurostructural patterns typical of older brains. Future research could utilise transdiagnostic samples with information on age of onset and disorder duration to further clarify this relation.

Although music has been associated with increased prosocial behavior, the underlying mechanisms for music-facilitated social benefits are not known. We test the hypothesis that chord progressions promote social bonding between dyads by shared temporal alignment of frequency spectra. Two musical conditions were presented to 20 pairs of participants (equal numbers of males and females), one with either a structured chord or predictable progression and the other with an unstructured and unpredictable composition of the same notes. Functional near-infrared spectroscopy signals were recorded simultaneously from both partners during the music conditions with and without gazing at a live partner's face. The right angular gyrus, right somatosensory association cortex, and bilateral dorsal lateral prefrontal cortex increased activation during live face gaze combined with the structured chord progression condition. Further, subjective ratings of subjective connectedness were associated with both activity in the right superior and middle temporal gyri during face gaze and the right angular gyrus during chord progressions. These findings link live face-to-face gaze while listening to structured chord progressions to neural systems that are responsive to predictive alignment of co-occurring acoustic spectra and perceptions of social connectedness.

Mitochondrial oxidative dysfunction is increasingly implicated in the neuropathophysiology of bipolar disorder (BD), yet in vivo brain assessments of cerebral oxidative metabolism and its relationship with oxygenation have been limited. Here, brain broadband near-infrared spectroscopy (bNIRS) non-invasively measured oxidation of the key enzyme of the mitochondrial electron transport chain, mitochondrial complex IV cytochrome-c-oxidase (oxCCO), alongside oxyhemoglobin (HBO) and deoxyhemoglobin (HBR) measures, in vivo in adults with BD and healthy comparison (HC) during visual stimulation. The relationship between oxCCO and the hemodynamic measures was assessed. During visual stimulation, participants with BD showed significantly higher elevations in oxCCO and significantly lower relative power (concurrence between oxCCO and oxygen use) compared to HC participants, a pattern also observed in euthymic BD participants, suggesting a trait difference in BD. The BD participants also had significantly higher levels of peripheral blood lactate, even when oxCCO levels were high, unlike the association observed for HC participants, which would be expected if oxidative phosphorylation was providing energy to meet neural demands. Together, these findings suggest that to meet neural energy demands, mitochondria in BD exhibit oxygen consumption that is not efficiently coupled to ATP production, and instead shift toward an energetically inefficient process, aerobic glycolysis. By enabling the simultaneous assessment of mitochondrial metabolism and hemodynamics, bNIRS provided a new, scalable, non-invasive tool to uncover mechanisms of neuroenergetic dysfunction in BD. This approach may facilitate the identification of novel mechanistic targets and advance biomarker development for more personalized interventions in mood disorders and potentially other neuropsychiatric disorders.

Schizophrenia is a severe psychiatric disorder associated with a wide range of cognitive and neurophysiological dysfunctions and long-term social difficulties. Early detection is expected to reduce the burden of disease by initiating early treatment. In this paper, we test the hypothesis that integration of multiple simultaneous acquisitions of neuroimaging, behavioral, and clinical information will be better for prediction of early psychosis than unimodal recordings. We propose a novel framework to investigate the neural underpinnings of the early psychosis symptoms (that can develop into Schizophrenia with age) using multimodal acquisitions of neural and behavioral recordings including functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and facial features. Our data acquisition paradigm is based on live face-toface interaction in order to study the neural correlates of social cognition in first-episode psychosis (FEP). We propose a novel deep representation learning framework, Neural-PRISM, for learning joint multimodal compressed representations combining neural as well as behavioral recordings. These learned representations are subsequently used to describe, classify, and predict the severity of early psychosis in patients, as measured by the Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) scores to evaluate the impact of symptomatology. We found that incorporating joint multimodal representations from fNIRS and EEG along with behavioral recordings enhances classification between typical controls and FEP individuals (significant improvements between 10 − 20%). Additionally, our results suggest that geometric and topological features such as curvatures and path signatures of the embedded trajectories of brain activity enable detection of discriminatory neural characteristics in early psychosis.

Schizophrenia is a severe psychiatric disorder associated with a wide range of cognitive and neurophysiological dysfunctions and long-term social difficulties. In this paper, we test the hypothesis that integration of multiple simultaneous acquisitions of neuroimaging, behavioral, and clinical information will be better for prediction of early psychosis than unimodal recordings. We propose a novel framework to investigate the neural underpinnings of the early psychosis symptoms (that can develop into Schizophrenia with age) using multimodal acquisitions of neural and behavioral recordings including functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and facial features. Our data acquisition paradigm is based on live face-to-face interaction in order to study the neural correlates of social cognition in first-episode psychosis (FEP). We propose a novel deep representation learning framework, Neural-PRISM, for learning joint multimodal compressed representations combining neural as well as behavioral recordings. These learned representations are subsequently used to describe, classify, and predict the severity of early psychosis in patients, as measured by the Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) scores. We found that incorporating joint multimodal representations from fNIRS and EEG along with behavioral recordings enhances classification between typical controls and FEP individuals. Additionally, our results suggest that geometric and topological features such as curvatures and path signatures of the embedded trajectories of brain activity enable detection of discriminatory neural characteristics in early psychosis.

Social difficulties during interactions with others are central to autism spectrum disorder (ASD). Understanding the links between these social difficulties and their underlying neural processes is a primary aim focused on improved diagnosis and treatment. In keeping with this goal, we have developed a multivariate classification method based on neural data acquired by functional near infrared spectroscopy, fNIRS, during live eye-to-eye contact with adults who were either typically developed (TD) or individuals with ASD. The ASD diagnosis was based on the gold-standard Autism Diagnostic Observation Schedule (ADOS) which also provides an index of symptom severity. Using a nested cross-validation method, a support vector machine (SVM) was trained to discriminate between ASD and TD groups based on the neural responses during eye-to-eye contact. ADOS scores were not applied in the classification training. To test the hypothesis that SVM identifies neural activity patterns related to one of the neural mechanisms underlying the behavioral symptoms of ASD, we determined the correlation coefficient between the SVM scores and the individual ADOS scores. Consistent with the hypothesis, the correlation between observed and predicted ADOS scores was 0.72 (p < 0.002). Findings suggest that multivariate classification methods combined with the live interaction paradigm of eye-to-eye contact provide a promising approach to link neural processes and social difficulties in individuals with ASD.

It has long been understood that the ventral visual stream of the human brain processes features of simulated human faces. Recently, specificity for real and interactive faces has been reported in lateral and dorsal visual streams, raising new questions regarding neural coding of interactive faces and lateral and dorsal face-processing mechanisms. We compare neural activity during two live interactive face-to-face conditions where facial features and tasks remain constant while the social contexts (in-person or on-line conditions) are varied. Current models of face processing do not predict differences in these two conditions as features do not vary. However, behavioral eye-tracking measures showed longer visual dwell times on the real face and also increased arousal as indicated by pupil diameters for the real face condition. Consistent with the behavioral findings, signal increases with functional near infrared spectroscopy, fNIRS, were observed in dorsal-parietal regions for the real faces and increased cross-brain synchrony was also found within these dorsal-parietal regions for the real In-person Face condition. Simultaneously, acquired electroencephalography, EEG, also showed increased theta power in real conditions. These neural and behavioral differences highlight the importance of natural, in-person, paradigms and social context for understanding live and interactive face processing in humans.