scholarly journals Neural Activation via Acupuncture in Patients With Major Depressive Disorder: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Tingyu Zhang ◽  
Jiaqi Zhang ◽  
Jiaxi Huang ◽  
Zhong Zheng ◽  
Pu Wang
Keyword(s):  
Major Depressive Disorder ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Severe Depression ◽  
Single Session ◽  
Major Depressive ◽  
Functional Near Infrared Spectroscopy ◽  
Moderate Depression ◽  
Before And After ◽  
The Brain

Background and Objective: Acupuncture is used as an alternative treatment for patients with major depressive disorder (MDD). The associated therapeutic effect of acupuncture is often attributed to its modulatory effect on the activity of the pre-frontal cortex (PFC), although the mechanism is not well-studied. We employed a repeated measures design to investigate the brain modulatory effect of acupuncture on the PFC in a group of patients with MDD and investigated whether the modulatory effect is influenced by the severity of the disease.Methods: A total of 47 patients diagnosed with MDD were enrolled in this functional near-infrared spectroscopy experiment. The severity of depressive symptoms was measured at baseline using the Hamilton Depression Rating Scale-24 (HAMD). The cortical activation in the bilateral PFC areas during a verbal fluency task (VFT) was measured before and after a single session of acupuncture in the Baihui acupoint. We further explored the potential correlation between the severity of MDD and task-related activation before and after acupuncture.Results: A single session of acupuncture significantly tended to enhance the activation level of the left frontopolar cortex in patients with severe depression during VFT, but a null effect was found in those with mild to moderate depression. Among patients with severe depression, a strong correlation was observed between HAMD scores and the change in VFT-related activation after acupuncture in the left dorsolateral PFC (DLPFC).Conclusion: A single session of acupuncture did not significantly modulate the activation of the left PFC in patients with mild to moderate depression; however, it demonstrated a tendency to enhance the activation of the frontopolar area in patients with severe depression. Among patients with severe depression, there is a correlation between the activation by acupuncture of left DLPFC during executive functioning and the severity of depressive symptoms, suggesting that the brain activity induced by acupuncture is likely to be influenced by the baseline disease severity in patients with MDD.

scholarly journals Prefrontal Functional Connectivity During the Verbal Fluency Task in Patients With Major Depressive Disorder: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Suh-Yeon Dong ◽  
JongKwan Choi ◽  
Yeonsoo Park ◽  
Seung Yeon Baik ◽  
Minjee Jung ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Verbal Fluency ◽  
Near Infrared ◽  
Healthy Individuals ◽  
Major Depressive ◽  
Functional Near Infrared Spectroscopy ◽  
Verbal Fluency Task ◽  
Fluency Task

Deviations in activation patterns and functional connectivity have been observed in patients with major depressive disorder (MDD) with prefrontal hemodynamics of patients compared with healthy individuals. The graph-theoretical approach provides useful network metrics for evaluating functional connectivity. The evaluation of functional connectivity during a cognitive task can be used to explain the neurocognitive mechanism underlying the cognitive impairments caused by depression. Overall, 31 patients with MDD and 43 healthy individuals completed a verbal fluency task (VFT) while wearing a head-mounted functional near-infrared spectroscopy (fNIRS) devices. Hemodynamics and functional connectivity across eight prefrontal subregions in the two groups were analyzed and compared. We observed a reduction in prefrontal activation and weaker overall and interhemispheric subregion-wise correlations in the patient group compared with corresponding values in the control group. Moreover, efficiency, the network measure related to the effectiveness of information transfer, showed a significant between-group difference [t (71.64) = 3.66, corrected p < 0.001] along with a strong negative correlation with depression severity (rho = −0.30, p = 0.009). The patterns of prefrontal functional connectivity differed significantly between the patient and control groups during the VFT. Network measures can quantitatively characterize the reduction in functional connectivity caused by depression. The efficiency of the functional network may play an important role in the understanding of depressive symptoms.

Functional near-infrared spectroscopy during a decision-making task in patients with major depressive disorder

2020 ◽  
pp. 000486742097685
Author(s):  
Syeda F Husain ◽  
Samantha K Ong ◽  
Liu Cuizhen ◽  
Bach Tran ◽  
Roger C Ho ◽  
...  
Keyword(s):  
Decision Making ◽  
Major Depressive Disorder ◽  
Infrared Spectroscopy ◽  
Depressive Disorder ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Iowa Gambling Task ◽  
Major Depressive ◽  
Functional Near Infrared Spectroscopy ◽  
Decision Making Processes

Objective: Patients with major depressive disorder tend to exhibit poorer decision-making capacity than the general population, but neurobiological evidence is lacking. Functional near-infrared spectroscopy monitors changes in oxy-haemoglobin concentration in the cerebral cortex. It may provide an objective assessment of neurophysiological responses during decision-making processes. Thus, this study investigated the effect of major depressive disorder diagnosis and severity on prefrontal cortex activity during the Iowa gambling task. Methods: Right-handed healthy controls ( n = 25) and patients with major depressive disorder ( n = 25) were matched for age, gender, ethnicity and years of education in this cross-sectional study. Functional near-infrared spectroscopy signals and the responses made during a computerised Iowa gambling task were recorded. In addition, demographics, clinical history and symptom severity were noted. Results: Compared to healthy controls, patients with major depressive disorder had reduced haemodynamic response in several cortical regions of the frontal lobe (Hedge’s g range from 0.71 to 1.52; p values range from ⩽0.001 to 0.041). Among patients, mean oxy-haemoglobin declined with major depressive disorder severity in the right orbitofrontal cortex (Pearson’s r = −0.423; p = 0.024). Conclusion: Haemodynamic dysfunction of the prefrontal cortex during decision-making processes is associated with major depressive disorder diagnosis and severity. These neurophysiological alterations may have a role in the decision-making capacity of patients with major depressive disorder.

scholarly journals Integration of the Cortical Haemodynamic Response Measured by Functional Near-Infrared Spectroscopy and Amino Acid Analysis to Aid in the Diagnosis of Major Depressive Disorder

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1978
Author(s):  
Samantha K. Ong ◽  
Syeda F. Husain ◽  
Hai Ning Wee ◽  
Jianhong Ching ◽  
Jean-Paul Kovalik ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Infrared Spectroscopy ◽  
Amino Acid ◽  
Depressive Disorder ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Haemodynamic Response ◽  
Potential Candidate ◽  
Major Depressive ◽  
Functional Near Infrared Spectroscopy

Background: Major depressive disorder (MDD) is a debilitating condition with a high disease burden and medical comorbidities. There are currently few to no validated biomarkers to guide the diagnosis and treatment of MDD. In the present study, we evaluated the differences between MDD patients and healthy controls (HCs) in terms of cortical haemodynamic responses during a verbal fluency test (VFT) using functional near-infrared spectroscopy (fNIRS) and serum amino acid profiles, and ascertained if these parameters were correlated with clinical characteristics. Methods: Twenty-five (25) patients with MDD and 25 age-, gender-, and ethnicity-matched HCs were recruited for the study. Real-time monitoring of the haemodynamic response during completion of a VFT was quantified using a 52-channel NIRS system. Serum samples were analysed and quantified by liquid chromatography-mass spectrometry for amino acid profiling. Receiver-operating characteristic (ROC) curves were used to classify potential candidate biomarkers. Results: The MDD patients had lower prefrontal and temporal activation during completion of the VFT than HCs. The MDD patients had lower mean concentrations of oxy-Hb in the left orbitofrontal cortex (OFC), and lower serum histidine levels. When the oxy-haemoglobin response was combined with the histidine concentration, the sensitivity and specificity of results improved significantly from 66.7% to 73.3% and from 65.0% to 90.0% respectively, as compared to results based only on the NIRS response. Conclusions: These findings demonstrate the use of combination biomarkers to aid in the diagnosis of MDD. This technique could be a useful approach to detect MDD with greater precision, but additional studies are required to validate the methodology.

scholarly journals Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

2021 ◽  
Author(s):  
Faezeh Moradi ◽  
Shima T. Moein ◽  
Issa Zakeri ◽  
Kambiz Pourrezaei
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Near Infrared ◽  
Stimulus Condition ◽  
Functional Near Infrared Spectroscopy ◽  
Odor Detection ◽  
The Brain

AbstractAn objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment.These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

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