Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Effects of Sports Functional Food on Physical Function of Athletes under Ultrasound Observation

In order to improve the physical function of athletes under hypoxic training, the authors propose to observe the effect of functional food with active ingredients of polypeptide polyamines in deer antler on the physical function of athletes under ultrasound observation. According to the characteristics of physiological changes during hypoxic training, functional foods containing the active ingredients of polypeptide polyamines in deer antler were selected and given to athletes under simulated hypoxic training, observe the changes of red blood cells (RBC), hemoglobin (Hb), hematocrit (Hct), blood lactic acid, free radical metabolism and immune function of athletes, and musculoskeletal under ultrasound observation, discuss how to improve the physical function and athletic ability of athletes under hypoxic training. Experimental results show that athletes after 6 weeks of hypoxic training, red blood cells and hemoglobin were significantly increased, there was a significant difference compared to the control group (P < 0.05 or P < 0.01). After 6 weeks of hypoxic training, hemoglobin increased by 10.1%, a 5.6 percentage point increase compared to the control group. Conclusion. The antler peptides used by the authors can enhance the effect of hypoxic training.

Effect of Nutritional Protein Food on Metabolism and Physical Fitness of Wushu Athletes

In order to greatly improve the physical function of martial arts athletes, this topic studies the effect of high-protein food on the physical function of martial arts athletes. Forty-five athletes in martial arts events took 5 g of high-protein food every day, 6 times a week for 4weeks, and the left and right forearm, calcaneus bone mineral density, and venous blood was drawn to detect bone metabolism and biochemical indicators related to physical function. The experimental results showed that the bone mineral density of the right calcaneus of male martial arts athletes increased significantly after taking high-protein food, and the bone mineral density of left and right forearms and calcaneus of female martial arts athletes increased significantly. After taking high-protein food, the serum calcium and phosphorus of female athletes and the serum calcium of male athletes were significantly increased. Sex decreased, female athletes significantly decreased serum creatine kinase, and male athletes significantly increased IgM. It can be seen that taking high-protein food for 4 weeks has a certain improvement effect on the bone mineral density of female athletes’ forearm and calcaneus, but has little effect on the bone mineral density of male athletes’ forearm and calcaneus. It can be concluded that high-protein food has no adverse effect on athletes’ bone metabolism, blood biochemical indexes, and immune globulin, and can better maintain the physical function level of martial arts athletes.

fNIRS-based brain state transition features to signify functional degeneration after Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls.

APPROACH

In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls.

RESULTS

Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0:8200 and F score of 0:9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle.

SIGNIFICANCE

The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

Somatic symptom disorder: a scoping review on the empirical evidence of a new diagnosis

BACKGROUND

In 2013, the diagnosis of somatic symptom disorder (SSD) was introduced into the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). This review aims to comprehensively synthesize contemporary evidence related to SSD.

METHODS

A scoping review was conducted using PubMed, PsycINFO, and Cochrane Library. The main inclusion criteria were SSD and publication in the English language between 01/2009 and 05/2020. Systematic search terms also included subheadings for the DSM-5 text sections; i.e., diagnostic features, prevalence, development and course, risk and prognostic factors, culture, gender, suicide risk, functional consequences, differential diagnosis, and comorbidity.

RESULTS

Eight hundred and eighty-two articles were identified, of which 59 full texts were included for analysis. Empirical evidence supports the reliability, validity, and clinical utility of SSD diagnostic criteria, but the further specification of the psychological SSD B-criteria criteria seems necessary. General population studies using self-report questionnaires reported mean frequencies for SSD of 12.9% [95% confidence interval (CI) 12.5-13.3%], while prevalence studies based on criterion standard interviews are lacking. SSD was associated with increased functional impairment, decreased quality of life, and high comorbidity with anxiety and depressive disorders. Relevant research gaps remain regarding developmental aspects, risk and prognostic factors, suicide risk as well as culture- and gender-associated issues.

CONCLUSIONS

Strengths of the SSD diagnosis are its good reliability, validity, and clinical utility, which substantially improved on its predecessors. SSD characterizes a specific patient population that is significantly impaired both physically and psychologically. However, substantial research gaps exist, e.g., regarding SSD prevalence assessed with criterion standard diagnostic interviews.

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.

fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases

Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR ( N C R ¯ ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: N C R ¯ can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.

The relationship of cortical activity induced by pain stimulation with clinical and cognitive features of somatic symptom disorder: A controlled functional near infrared spectroscopy study

OBJECTIVE

The neurobiological correlates of Somatic Symptom Disorder (SSD) introduced in the DSM-5 has been the focus of a limited investigation. We aimed to examine the cortical response to painful stimuli and its relationship to symptom severity as well as cognitive and psychological characteristics in proposed models of somatoform disorders.

METHODS

We measured hemodynamic responses by 52-channel functional near-infrared spectroscopy. We compared the cortical response to painful stimuli in index patients with SSD (N = 21) versus age, and gender matched healthy control subjects (N = 21). We used brush stimulation as the control condition. We analyzed the relationship of cortical activity with SSD symptom severity as well as somatosensory amplification (SSA), alexithymia, dysfunctional illness behaviour, worry, and neuroticism.

RESULTS

Patients with SSD had higher somatic symptom severity, SSA, alexithymia, neuroticism, illness-related worry, and behaviour. Somatic symptom severity was predicted by a model including SSA and subjective feeling of pain in the index patients. Activity in the left-angular and right-middle temporal gyri was higher in the SSD subjects than the controls during pain stimulation. Positive correlations were detected between mean pain threshold levels and left middle occipital gyrus activity, as well as between SSA-scores and right-angular gyrus activity during pain condition in the index patients with SSD.

CONCLUSION

We present the first evidence that representation of pain in terms of cortical activity is different in subjects with SSD than healthy controls. SSA has functional neuroanatomic correlates and predicts symptom severity in SSD and therefore is involved as a valid intermediate phenotype in SSD pathophysiology.

NIRS measures in pain and analgesia: Fundamentals, features, and function

Current pain assessment techniques based only on clinical evaluation and self-reports are not objective and may lead to inadequate treatment. Having a functional biomarker will add to the clinical fidelity, diagnosis, and perhaps improve treatment efficacy in patients. While many approaches have been deployed in pain biomarker discovery, functional near-infrared spectroscopy (fNIRS) is a technology that allows for non-invasive measurement of cortical hemodynamics. The utility of fNIRS is especially attractive given its ability to detect specific changes in the somatosensory and high-order cortices as well as its ability to measure (1) brain function similar to functional magnetic resonance imaging, (2) graded responses to noxious and innocuous stimuli, (3) analgesia, and (4) nociception under anesthesia. In this review, we evaluate the utility of fNIRS in nociception/pain with particular focus on its sensitivity and specificity, methodological advantages and limitations, and the current and potential applications in various pain conditions. Everything considered, fNIRS technology could enhance our ability to evaluate evoked and persistent pain across different age groups and clinical populations.