Modeling Social Sensory Processing During Social Computerized Cognitive Training for Psychosis Spectrum: The Resting-State Approach

Early auditory processing abnormalities alter individual learning trajectories and sensitivity to computerized cognitive training in schizophrenia

BACKGROUND

Auditory system plasticity is a promising target for neuromodulation, cognitive rehabilitation and therapeutic development in schizophrenia (SZ). Auditory-based targeted cognitive training (TCT) is a 'bottom up' intervention designed to enhance the speed and accuracy of auditory information processing, which has been shown to improve neurocognition in certain SZ patients. However, the dynamics of TCT learning as a function of training exercises and their impact on neurocognitive functioning and therapeutic outcomes are unknown.

METHODS

Forty subjects (SZ, n = 21; healthy subjects (HS), n = 19) underwent comprehensive clinical, cognitive, and auditory assessments, including measurements of auditory processing speed (APS) at baseline and after 1-h of TCT. SZ patients additionally completed 30-hours of TCT and repeated assessments ~10-12 weeks later.

RESULTS

SZ patients were deficient in APS at baseline (d = 0.96, p < 0.005) relative to HS. After 1-h of TCT, analyses revealed significant main effects of diagnosis (d = 1.75, p = 0.002) and time (d = 1.04, p < 0.001), and a diagnosis × time interaction (d = 0.85, p = 0.02) on APS. APS learning effects were robust after 1-h in SZ patients (d = 1.47, p < 0.001) and persisted throughout the 30-h of training. Baseline APS was associated with verbal learning gains after 30-h of TCT (r = 0.51, p = 0.02) in SZ.

CONCLUSIONS

TCT learning metrics may have prognostic utility and aid in the prospective identification of individuals likely to benefit from TCT. Future experimental medicine studies may advance predictive algorithms that enhance TCT-related clinical, cognitive and functional outcomes.

Disrupting nociceptive information processing flow through transcranial focused ultrasound neuromodulation of thalamic nuclei

BACKGROUND

MRI-guided transcranial focused ultrasound (MRgFUS) as a next-generation neuromodulation tool can precisely target and stimulate deep brain regions with high spatial selectivity. Combined with MR-ARFI (acoustic radiation force imaging) and using fMRI BOLD signal as functional readouts, our previous studies have shown that low-intensity FUS can excite or suppress neural activity in the somatosensory cortex.

OBJECTIVE

To investigate whether low-intensity FUS can suppress nociceptive heat stimulation-induced responses in thalamic nuclei during hand stimulation, and to determine how this suppression influences the information processing flow within nociception networks.

FINDINGS

BOLD fMRI activations evoked by 47.5 °C heat stimulation of hand were detected in 24 cortical regions, which belong to sensory, affective, and cognitive nociceptive networks. Concurrent delivery of low-intensity FUS pulses (650 kHz, 550 kPa) to the predefined heat nociceptive stimulus-responsive thalamic centromedial_parafascicular (CM_para), mediodorsal (MD), ventral_lateral (VL_ and ventral_lateral_posteroventral (VLpv) nuclei suppressed their heat responses. Off-target cortical areas exhibited reduced, enhanced, or no significant fMRI signal changes, depending on the specific areas. Differentiable thalamocortical information flow during the processing of nociceptive heat input was observed, as indicated by the time to reach 10% or 30% of the heat-evoked BOLD signal peak. Suppression of thalamic heat responses significantly altered nociceptive processing flow and direction between the thalamus and cortical areas. Modulation of contralateral versus ipsilateral areas by unilateral thalamic activity differed. Signals detected in high-order cortical areas, such as dorsal frontal (DFC) and ventrolateral prefrontal (vlPFC) cortices, exhibited faster response latencies than sensory areas.

CONCLUSIONS

The concurrent delivery of FUS suppressed nociceptive heat response in thalamic nuclei and disrupted the nociceptive network. This study offers new insights into the causal functional connections within the thalamocortical networks and demonstrates the modulatory effects of low-intensity FUS on nociceptive information processing.

Cognitive Performance and Learning Parameters Predict Response to Working Memory Training in Parkinson's Disease

BACKGROUND

Working memory (WM) training (WMT) is a popular intervention approach against cognitive decline in patients with Parkinson's disease (PD). However, heterogeneity in WM responsiveness suggests that WMT may not be equally efficient for all patients.

OBJECTIVE

The present study aims to evaluate a multivariate model to predict post-intervention verbal WM in patients with PD using a supervised machine learning approach. We test the predictive potential of novel learning parameters derived from the WMT and compare their predictiveness to other more commonly used domains including demographic, clinical, and cognitive data.

METHODS

37 patients with PD (age: 64.09±8.56, 48.6% female, 94.7% Hoehn & Yahr stage 2) participated in a 5-week WMT. Four random forest regression models including 1) cognitive variables only, 2) learning parameters only, 3) both cognitive and learning variables, and 4) the entire set of variables (with additional demographic and clinical data, 'all' model), were built to predict immediate and 3-month-follow-up WM.

RESULT

The 'all' model predicted verbal WM with the lowest root mean square error (RMSE) compared to the other models, at both immediate (RMSE = 0.184; 95% -CI=[0.184;0.185]) and 3-month follow-up (RMSE = 0.216; 95% -CI=[0.215;0.217]). Cognitive baseline parameters were among the most important predictors in the 'all' model. The model combining cognitive and learning parameters significantly outperformed the model solely based on cognitive variables.

CONCLUSION

Commonly assessed demographic, clinical, and cognitive variables provide robust prediction of response to WMT. Nonetheless, inclusion of training-inherent learning parameters further boosts precision of prediction models which in turn may augment training benefits following cognitive interventions in patients with PD.

Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.