Toward bedside neuroimaging in schizophrenia: longitudinal monitoring of clinical and cognitive improvement and DMN activity using fNIRS-EEG

Resting-state functional connectivity is widely used to assess brain network organization, typically via functional magnetic resonance imaging [1], although its clinical applicability remains limited due to cost and accessibility. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) offer portable, complementary measures of hemodynamic and electrophysiological activity, yet remain underexplored for longitudinal clinical use in schizophrenia [2]. This pilot study evaluates the feasibility of combined fNIRS/EEG for bedside assessment, focusing on default mode network (DMN) alterations, and examines whether this multimodal approach can detect longitudinal changes in clinical state and enable clinically feasible monitoring of network connectivity in schizophrenia.

Resting-state eyes-closed fNIRS/EEG data were acquired from 10 patients with schizophrenia (43 ± 11 years) and 10 age-matched healthy controls (40 ± 13 years). Patients were assessed at admission and after approximately six weeks of inpatient treatment; controls underwent one assessment. fNIRS targeted DMN regions using an eight-source, eight-detector montage, acquired simultaneously with 32-channel EEG. After artifact removal, EEG power spectral density was estimated using Welch’s method [3]. fNIRS (fALFF) was calculated from oxyhemoglobin signals [4], [5]. Group comparisons were conducted using Brunner–Munzel and Wilcoxon signed-rank tests with permutation testing. Clinical and cognitive associations were assessed using Spearman correlations on longitudinal change scores.

Patients showed clinically meaningful improvement, reflected by reduced Positive and Negative Syndrome Scale and Montgomery–Åsberg Depression Rating Scale scores, and improved Rey Auditory Verbal Learning Test performance. These changes were accompanied by converging neurophysiological trends. At baseline, EEG showed significantly reduced central alpha and prefrontal beta power in patients compared with healthy controls. After treatment, both measures increased, suggesting partial normalization, although not significantly. Right-temporal beta power significantly decreased from baseline to follow-up, indicating a shift toward the healthy-control pattern. fNIRS showed elevated fALFF across DMN-related regions at baseline, with follow-up values shifting toward healthy-control levels. Longitudinal EEG theta and fNIRS fALFF changes were associated with improved depressive symptoms and cognitive performance.

These findings demonstrate the feasibility of combined resting-state fNIRS/EEG for bedside assessment in schizophrenia. Clinical and neuropsychological improvements were accompanied by convergent electrophysiological and hemodynamic changes, supporting the sensitivity of this multimodal approach to clinically relevant dynamics. The observed DMN-related alterations align with prior evidence of disrupted resting-state connectivity in schizophrenia and its association with symptoms and cognition. The portability of fNIRS, combined with EEG’s temporal resolution, supports its potential for real-world monitoring. While limited by sample size, these results provide preliminary support for fNIRS/EEG as a tool for longitudinal assessment, warranting validation in larger cohorts.

Table: Longitudinal changes in electroencephalography and functional near-infrared spectroscopy measures and their association with clinical outcomes in schizophrenia

Domain	Measure	Baseline Mean±SD	Follow-up Mean±SD	Statistic Z	p-value
Clinical	MADRS	12.60±6.82	8.60±7.44	1.66	0.048
Cognitive	RAVLT Delayed Recall	58.50±21.04	65.20±18.25	1.97	0.049
EEG	Alpha Power(Central)	0.283±0.090	0.296±0.129	0.59	0.557
EEG	Beta Power(Prefrontal)	0.120±0.079	0.151±0.116	0.79	0.432
EEG	Beta Power(Right Temporal)	0.239±0.153	0.172±0.090	1.96	0.039
fNIRs	fALFF(DMN slow-4, 0.027-0.08 Hz)	0.190±0.082	0.164±0.046	1.60	0.109
fNIRs	fALFF(DMN slow-5, 0.01-0.027)	0.183±0.042	0.167±0.045	1.46	0.250
Association	EEG Theta vs MADRS			ρ=-0.71	0.0297
Association	fNIRS fALFF vs RAVLT			ρ=0.79	0.036

References

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