Explore the vast range of titles available.

Bipolar disorder (BD) patients with psychotic symptoms (BDp) worsens prognosis and decreases rates of recovery. The study investigated cognitive performance and brain function between BD patients in depressive episode with and without psychotic symptoms to find potential neurobiological markers associated with psychotic features of BD patients in depressive episode. Thirty-one patients without psychotic symptoms and 29 patients with psychotic symptoms diagnosed with bipolar I disorder with a current depressive episode were included in the present study. The neural function of prefrontal cortex (PFC) and cognitive performance among BDp, BD patients without psychotic symptoms, and 23 healthy controls (HC) were examined using near-infrared spectroscopy during verbal fluency task (VFT). 1) Both the BD groups exhibited significantly worse performance of VFT and lower activation of oxygenated hemoglobin (oxy-Hb) changes in the bilateral ventrolateral PFC compared with the HC group. 2) Within the BD group, VFT performance was not significantly different. 3) The prefrontal activation of oxy-Hb changes in the BDp patients was significantly lower than that in the BD patients without psychotic symptoms in the right dorsolateral PFC. 4) Activation of oxy-Hb changes in right dorsolateral PFC was negatively correlated with the severity of psychotic symptoms in BDp patients. The prefrontal function differs between BD patients in depressive episode with or without psychotic symptoms measured with near-infrared spectroscopy.

People and other animals can search for information inside their heads. Where does this ability come from, and what does it enable cognitive systems to do? In this article, we address the behavioral and cognitive similarities between search in external environments and internal environments (e.g., memory). These require both maplike representations and the means to navigate them, and the latter involves modulation between exploitation and exploration analogous to a foraging process called area-restricted search. These findings have implications for understanding a number of cognitive abilities commonly considered to be hallmarks of the human species, such as well-developed executive control and goal-directed cognition, autonoetic consciousness (i.e., self-awareness), deliberation, and free will. Moreover, this research extends our conception of what organisms may share these abilities and how they evolved.

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.

Brain activity during a verbal fluency task (VFT) has been the target of many functional imaging studies. Most studies using near-infrared spectroscopy (NIRS) have reported major activation in the frontal pole, but those using PET or fMRI have not. This led us to hypothesize that changes in the NIRS signals measured in the forehead during VFT were due to changes in skin blood flow. To test this hypothesis, we measured NIRS signals and the Doppler tissue blood flow signals in the foreheads of 50 participants. The measurements were performed while each participant produced words during two 60-s periods with an interval of 100s. In addition to a conventional optode separation distance of 30mm (FAR channels), we used a short distance – 5mm (NEAR channels) – to measure NIRS signals that originated exclusively from surface tissues. The oxygenated hemoglobin (oxyHb) concentration in the FAR and NEAR channels, as well as the Doppler blood flow signal, increased in a similar manner during the two periods of word production; the signal increase in the first period was twice as high as that in the second period. Accordingly, the mean changes in oxyHb concentration in the FAR channels were correlated closely with the changes in the NEAR channels (R2=0.91) and with the integrated Doppler skin blood flow signal (R2=0.94). Furthermore, task-related NIRS responses disappeared when we blocked skin blood flows by pressing a small area that covered a pair of optodes. Additionally, changes in the FAR channel signals were correlated closely with the magnitude of pulsatile waves in the Doppler signal (R2=0.92), but these signals were not highly correlated with the pulse rate (R2=0.43). These results suggest that a major part of the task-related changes in the oxyHb concentration in the forehead is due to task-related changes in the skin blood flow, which is under different autonomic control than heart rate. ► NIRS signals on the forehead are due to the VFT-related changes in skin blood flow. ► Pulsatile magnitude in Doppler signal can explain NIRS response. ► Pulsatile rate in Doppler signal did not correlate well with the NIRS response. ► The VFT-related NIRS signals would be under different autonomic control than HR.

This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar depression (UD) and bipolar depression (BD) using functional near-infrared spectroscopy (fNIRS) during a verbal fluency task (VFT). Additionally, it evaluated the reliability of fNIRS as a diagnostic tool for cognitive assessments through a deep learning approach using one-dimensional convolutional networks. The study included 73 patients with UD, 59 patients with BD, and 40 healthy controls (HC). Hemodynamic responses in the prefrontal cortex were recorded using fNIRS during the VFT. Differences in oxygenated hemoglobin concentrations across the three groups were compared, and receiver operating characteristic (ROC) curves were generated for each region of interest. Both UD and BD patients demonstrated significantly reduced activation in the prefrontal cortex compared to healthy controls. UD patients showed notably lower activation values than BD patients in the dorsolateral prefrontal cortex, frontopolar prefrontal cortex, left orbitofrontal cortex, and left ventrolateral prefrontal cortex. The highest classification accuracy (79.57%) was observed in the left orbitofrontal cortex. The UD group had the largest area under the ROC curve (AUC = 0.99) in the left orbitofrontal cortex, while the BD group had the largest AUC (0.91) in the right dorsolateral prefrontal cortex. The HC group exhibited the largest AUC (0.73) in the same region. The DLPFC, FPC, lOFC, and lVLPFC may serve as biomarker regions for differentiating UD from BD. The combination of fNIRS and the VFT shows promise as a supplementary diagnostic tool for mental health disorders.

To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a “shared component” (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source–detector (S–D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S–D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S–D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation performance of our method, we used the correlation coefficients of a laser-Doppler flowmetry (LDF) signal and a nearest 5-mm S–D distance channel signal with the shallow signal. We demonstrated that the shallow signals have a higher temporal correlation with the LDF signals and with the 5-mm S–D distance channel than the deep signals. These results show the MD-ICA method can discriminate between deep and shallow signals. [Display omitted] ► Method for evaluating deep/shallow-tissue contribution to fNIRS signal is proposed. ► We assume both deep and shallow signals are linear combinations of multiple sources. ► Multi-distance optodes and ICA are combined for the proposed method (MD-ICA). ► The method is demonstrated with a dynamic phantom and human brain measurements. ► MD-ICA can be applied to data that is highly correlated between deep/shallow signals.

To investigate the hemodynamic differences in various brain regions between alcohol dependence (AlcD) patients and healthy controls during a verbal fluency task (VFT) using functional near-infrared spectroscopy (fNIRS), and to further explore the clinical predictive value of fNIRS before therapy for the outcome of relapse in AlcD patients after 3 months. A retrospective survey was conducted on 123 AlcD patients and 149 healthy controls during the same period. Baseline assessment of fNIRS was performed to analyze the hemodynamic differences between the two groups in different brain regions. During hospitalization, AlcD patients underwent a 3-week benzodiazepine substitution therapy, gradually tapering off the medication to achieve alcohol withdrawal treatment goals. Three months after discharge, we conducted follow-up phone calls to assess the relapse status of the patients. Compared to the control group, the AlcD group had significantly lower integral values in the frontal and bilateral temporal lobes, as well as lower β-values in all channels of the frontal lobe except for Ch13, and in all channels of the bilateral temporal lobes ( p  < 0.005), with no significant difference in the parietal lobe channel( p  > 0.05). ROC (Receiver Operating Characteristic Curve) analysis for predicting relapse within 3 months showed that the area under the curve for all channels was highest (0.951, sensitivity 0.924, specificity 0.886). Patients with AlcD exhibit functional impairments in the frontal and temporal lobes. fNIRS channels in the frontal and parietal lobes based on VFT have good clinical predictive value for relapse within 3 months after pharmacotherapy in AlcD and can be applied in clinical practice.

Children with High-Functioning Autism (HFA) often show marked deficits in executive functioning, particularly during verbal fluency tasks (VFTs). These behavioral impairments are believed to stem from neurophysiological abnormalities in the prefrontal cortex (PFC) functioning, characterized by atypical activation patterns and disrupted functional connectivity. This study utilized functional near-infrared spectroscopy (fNIRS) to investigate hemodynamic responses and connectivity metrics during VFT performance. By comparing children with HFA to age-matched typically developing (TD) controls, this study aimed to clarify the neural mechanisms underlying the executive control of language production in HFA. The sample included 29 children who met diagnostic criteria for HFA and 26 TD controls. All participants had a Full-Scale Intelligence Quotient of 70 or higher and were matched for age and cognitive ability. During a standardized phonemic VFT, cortical hemodynamics were continuously monitored using a 19-channel fNIRS system, with analyses focusing on changes in oxygenated hemoglobin concentration within PFC regions. Compared with TD controls, children with HFA exhibited reduced cortical activation across multiple prefrontal regions, including channels 1 ( = -2.975, = 0.017), 2 ( = -4.320, = 0.001), 3 ( = -3.381, = 0.012), 9 ( = -3.127, = 0.014), and 19 ( = -3.279, = 0.012). These regions correspond anatomically to the inferior prefrontal gyrus, frontopolar cortex, and dorsolateral PFC. Functional connectivity analyses demonstrated significantly reduced interregional coupling in the HFA group (  < 0.001), with mean connectivity values of 0.512 (SD = 0.076) compared with 0.566 (SD = 0.069) in TD participants. Furthermore, Oxy-Hb changes in prefrontal channels 1 ( = -0.424, = 0.022), 2 ( = -0.432, = 0.019), and 3 ( = -0.394, = 0.034) were negatively correlated with Social Responsiveness Scale total scores, indicating that weaker prefrontal activation was associated with greater social impairment. The results reveal distinct cortical activation and functional connectivity alterations in children with HFA during VFTs. These findings support the hypothesis that disrupted interregional brain coordination underlies executive difficulties in language production in HFA children, who exhibit reduced PFC activation and weaker interregional functional connectivity during the VFT.

Background Previous studies have shown that insomnia affects human prefrontal function and that there are specific patterns of brain activation to counteract sleep and improve cognition. However, the effects of insomnia on the prefrontal cortex of MDD (major depressive disorder) patients and the patterns of activation to counteract sleep in MDD patients remain unclear. The aim of this study is to examine this using fNIRS (functional near-infrared spectroscopy). Methods Eighty depressed patients and 44 healthy controls were recruited for this study. fNIRS was used to assess changes in the concentration of oxygenated hemoglobin ([oxy-Hb]) in the prefrontal cortex of all participants during the VFT (verbal fluency test) and to record the number of words created to assess cognitive ability. The Pittsburgh Sleep Quality Index was used to assess sleep quality, and the Hamilton Rating Scale for Depression (24-item) and Hamilton Rating Scale for Anxiety (14-item) were used to assess the severity of depression and anxiety. Results When comparing patients, the healthy control group had significantly higher [oxy-Hb] values in the bilateral prefrontal cortex during VFT than the MDD group. In the MDD group, the [oxy-Hb] values in all brain regions except the right DLPFC were significantly higher in the group with insomnia than in the group without insomnia, but their VFT performance was significantly lower than in the group without insomnia and the healthy group. PSQI scores were positively correlated with [oxy-Hb] values in some left-brain regions, whereas HAMD and HAMA scores were not correlated with [oxy-Hb] values. Conclusion The PFC was significantly less active during VFT in those with MDD than in healthy controls. All brain regions, except the right DLPFC, were significantly more active in MDD patients with insomnia than in those without insomnia, suggesting that sleep quality needs to be an important indicator in fNIRS screening. In addition, there was a positive correlation between the severity of insomnia in the left VLPFC and the level of activation, suggesting a role for the left brain region in the neurophysiology of overcoming sleepiness in MDD patients. these findings may provide new ideas for the treatment of MDD patients in the future. Trial registration Our experiment was registered in the China Clinical Trial Registry (registration number ChiCTR2200065622) on November 10.( The first patient was recruited in 10/11/2022.)

To examine prefrontal hemodynamic changes in patients with post-stroke anxiety (PSA), both at rest and during cognitive task engagement, with the aim of elucidating the underlying neural mechanisms of PSA and identifying potential neural correlates for clinical application. Fifty patients with PSA and 45 post-stroke patients without anxiety symptoms were recruited. PSA was diagnosed using the Hamilton Anxiety Rating Scale (HAMA ≥ 7), and comorbid depression was screened using the 17-item Hamilton Depression Rating Scale (HAMD-17 ≥ 8). Patients with significant cognitive impairment were excluded. Functional near-infrared spectroscopy (fNIRS) was used to measure resting-state functional connectivity in the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC), as well as task-evoked activation during the verbal fluency task (VFT). Demographic and clinical characteristics showed no significant differences between groups except for stroke type. Between-group comparisons were conducted to identify PSA-related differences in prefrontal network characteristics. Subgroup analyses were performed to explore the influence of comorbid depression on neural alterations. There were no significant differences between the PSA and non-PSA groups in demographic or clinical characteristics, including age, sex, and disease duration ( > 0.05). Compared to the non-PSA group, patients with PSA exhibited significantly reduced activation in the bilateral FPC during the VFT ( < 0.05). Within the PSA group, those with comorbid depression showed further reductions in activation in the bilateral FPC and the left DLPFC ( < 0.05). No significant differences in resting-state functional connectivity were observed between groups ( > 0.05). Reduced activation in the bilateral FPC may represent a key neural substrate associated with post-stroke anxiety. In addition, altered activation patterns in the bilateral FPC and left DLPFC may reflect neural correlates related to depressive symptoms in patients with PSA, providing candidate targets for future mechanistic and clinical studies.