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Clinical value of predicting relapse within 3 months in alcohol-dependent patients using fNIRS in verbal fluency task

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Title: Clinical value of predicting relapse within 3 months in alcohol-dependent patients using fNIRS in verbal fluency task
Authors: Anqi Huang, Ran Wang, Aiping Wen, Lin Xu, Na Li, Yuanyuan Gao, Wenting Lu, Shijie Guo, Jincheng Wang, Lan Wang
Source: Scientific Reports, Vol 15, Iss 1, Pp 1-13 (2025)
Publisher Information: Nature Portfolio, 2025.
Publication Year: 2025
Collection: LCC:Medicine
LCC:Science
Subject Terms: Alcohol dependence, Functional near-infrared spectroscopy, Verbal fluency task, Relapse, Medicine, Science
More Details: Abstract 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 0.05). 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-025-89775-7
Access URL: https://doaj.org/article/1d2c47a7b2bc483ca99856a2f8035585
Accession Number: edsdoj.1d2c47a7b2bc483ca99856a2f8035585
Database: Directory of Open Access Journals
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  Value: <anid>AN0182974355;[fkqs]12feb.25;2025Feb14.01:25;v2.2.500</anid> <title id="AN0182974355-1">Clinical value of predicting relapse within 3 months in alcohol-dependent patients using fNIRS in verbal fluency task </title> <p>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.</p> <p>Keywords: Alcohol dependence; Functional near-infrared spectroscopy; Verbal fluency task; Relapse; Medical and Health Sciences Clinical Sciences</p> <hd id="AN0182974355-2">Introduction</hd> <p>Alcohol dependence is a psychiatric disorder associated with a high mortality rate, causing a multitude of somatic diseases and accompanied by cognitive impairments, which hinder normal work and social interactions. The rate of relapse after initial pharmacotherapy can be as high as 85%, posing a significant challenge to mental health [<reflink idref="bib1" id="ref1">1</reflink>], [<reflink idref="bib2" id="ref2">2</reflink>], [<reflink idref="bib3" id="ref3">3</reflink>]–[<reflink idref="bib4" id="ref4">4</reflink>]. The high rate of relapse presents a considerable challenge for clinicians, and predicting short-term relapse in patients with alcohol dependence could provide a theoretical basis for subsequent proactive interventions.</p> <p>Previous research suggests that the high relapse rate in alcohol dependence may be related to damage in the neural circuits associated with reward and executive control, involving changes in brain structure and functional impairments [<reflink idref="bib5" id="ref5">5</reflink>], [<reflink idref="bib6" id="ref6">6</reflink>], [<reflink idref="bib7" id="ref7">7</reflink>]–[<reflink idref="bib8" id="ref8">8</reflink>]. Structural MRI, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have been widely used to investigate the thickness of the cerebral cortex, gray matter volume, and neural network patterns in alcohol dependence [<reflink idref="bib9" id="ref9">9</reflink>],[<reflink idref="bib10" id="ref10">10</reflink>]. The limitation of fMRI is the temporal resolution. Data collection is slowed down by the fact that blood flow changes take several seconds and the actual recording is limited by computational factors. The limitation of EEG, on the other hand, is the spatial resolution. Since the electrodes measure electrical activity on the surface of the brain, it is difficult to know whether the signals are generated in the cerebral cortex or in deeper brain regions[<reflink idref="bib11" id="ref11">11</reflink>],[<reflink idref="bib12" id="ref12">12</reflink>]. In recent years, functional near infrared spectroscopy (fNIRS) has become more common in clinical applications[<reflink idref="bib13" id="ref13">13</reflink>],[<reflink idref="bib14" id="ref14">14</reflink>]. fNIRS does not have stringent requirements for the subjects, offering advantages such as simple operation, convenience, comfort, and easy acquisition. The principle of fNIRS is similar to that of fMRI, that is, neural activity in the brain leads to local haemodynamic changes. fNIRS can be combined with various cognitive task paradigms for research. During cognitive activities, neurons in the cerebral cortex consume oxygen carried by hemoglobin in nearby blood vessels. fNIRS detects changes in the concentration of oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in the blood within the cerebral cortex by measuring the absorption of light, reflecting the neuronal activity at the detection points. Among these techniques, the greatest advantage of fNIRS is that its temporal resolution is faster than that of fMRI, its spatial resolution is larger than that of EEG, and more importantly, it is portable and has fewer artifacts. Currently, fNIRS is widely used in the research of psychiatric disorders for diagnosis, differential diagnosis, and predicting treatment efficacy [<reflink idref="bib15" id="ref15">15</reflink>], [<reflink idref="bib16" id="ref16">16</reflink>], [<reflink idref="bib17" id="ref17">17</reflink>]–[<reflink idref="bib18" id="ref18">18</reflink>]. However, there is limited research on fNIRS in the context of alcohol dependence. A recent study by Guo et al. used fNIRS to investigate the mechanisms of social cognitive impairment in person with alcohol dependence, finding a negative correlation between inter-brain synchronization in the right medial frontal cortex during cooperation tasks and unplanned impulsivity [<reflink idref="bib19" id="ref19">19</reflink>].</p> <p>The verbal fluency task (VFT) is widely used in neuropsychological assessments to reflect cognitive, memory, and executive functions of the brain[<reflink idref="bib20" id="ref20">20</reflink>], [<reflink idref="bib21" id="ref21">21</reflink>]–[<reflink idref="bib22" id="ref22">22</reflink>]. VFT chosen for this study is a task paradigm that reflects an individual's language ability, specifically language production. Research has shown that the activation of the local brain cortex during the VFT primarily occurs in the left frontal and temporal lobes[<reflink idref="bib23" id="ref23">23</reflink>]. This is consistent with the role of the left hemisphere in language dominance, which is responsible for functions such as semantic storage, recognition, and executive initiation. The VFT consists of two main task modes: both task paradigms reflect cognitive processing, but with different emphases[<reflink idref="bib24" id="ref24">24</reflink>]. Semantic fluency is influenced by the structural damage to semantic knowledge and is more related to damage in the temporal cortex[<reflink idref="bib25" id="ref25">25</reflink>]. On the other hand, phonemic fluency tasks rely on the use of phonetic or lexical cue strategies and are associated with frontal executive functions[<reflink idref="bib26" id="ref26">26</reflink>]. Phonemic fluency tasks become challenging when there is damage to the frontal lobe. Therefore, the sensitivity of VFT to different diseases may vary. Schecklmann et al. confirmed that compared to healthy controls, patients with alcohol dependence showed reduced brain activation during the VFT, with activation more localized to the subfrontal area [<reflink idref="bib27" id="ref27">27</reflink>]. Research by Nowakowska and others [<reflink idref="bib28" id="ref28">28</reflink>] has shown that although patients with alcohol dependence exhibit varying degrees of cognitive dysfunction, frontal lobe impairments persist for at least a year after alcohol withdrawal. Short-term abstinence (3 days) does not improve non-verbal executive and planning abilities, but verbal fluency, language control, and memory do show improvement. Combining fNIRS with VFT allows for the exploration of changes in cognitive function in patients with alcohol dependence [<reflink idref="bib29" id="ref29">29</reflink>],[<reflink idref="bib30" id="ref30">30</reflink>]. We considered that frontal lobe function fNIRS signalling activation is worse in alcohol-dependent patients, and that there may be widespread inhibition of neural activity, so we used a phonotactic task to test how haemodynamics change during brain activity in alcohol-dependent patients. Considering that the VFT task is able to activate haemodynamic changes in important brain regions such as the prefrontal and temporal lobes, we hypothesised that the fNIRs of AlcD may suggest poorer signal activation compared to healthy controls, and that observing whether there is a pattern to these differences could lead us to an indicator of redrinking.</p> <p>Therefore, this study aims to conduct a retrospective investigation to explore the hemodynamic changes in various brain regions and the differences in channel activation between patients with alcohol dependence and healthy controls using fNIRS combined with VFT. Furthermore, it seeks to explore the clinical predictive value of fNIRS before treatment for the outcome of relapse in patients with alcohol dependence.</p> <hd id="AN0182974355-3">Methods</hd> <p></p> <hd id="AN0182974355-4">Study participants</hd> <p>The alcohol dependence group included patients treated for alcohol dependence at the Mental Health Center of the First Hospital of Hebei Medical University from August to December 2017. Inclusion criteria were as follows: (<reflink idref="bib1" id="ref31">1</reflink>) meeting the diagnostic criteria for alcohol dependence syndrome according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) : dependence syndrome by meeting three or more of the following criteria: a. have strong cravings and urges to use alcohol; b. have difficulty in controlling the onset, end, and dosage of the behaviour of alcohol use; c. experience a physical withdrawal response; d. tolerance; e. gradual loss of other pleasures or interests as a result of alcohol use; f. stubborn use of alcohol without regard to the obvious dangerous consequences. In addition, scoring ≥ 12 on the Alcohol Dependence Scale (ADS) [<reflink idref="bib31" id="ref32">31</reflink>]; (<reflink idref="bib2" id="ref33">2</reflink>) aged between 18 and 70 years; (<reflink idref="bib3" id="ref34">3</reflink>) male; (<reflink idref="bib4" id="ref35">4</reflink>) right-handed; (<reflink idref="bib5" id="ref36">5</reflink>) willing and able to comply with the study procedures. The healthy control group consisted of individuals undergoing physical examination at the same hospital during the same period, with inclusion criteria including: (<reflink idref="bib1" id="ref37">1</reflink>) not meeting any psychiatric disorder diagnosis according to ICD-10; (<reflink idref="bib2" id="ref38">2</reflink>) non-drinkers or occasional social drinkers (≤ 2 drinking occasions per year); (<reflink idref="bib3" id="ref39">3</reflink>) aged between 18 and 70 years; (<reflink idref="bib4" id="ref40">4</reflink>) male; (<reflink idref="bib5" id="ref41">5</reflink>) right-handed; (<reflink idref="bib6" id="ref42">6</reflink>) willing and able to comply with the study procedures.</p> <p>Exclusion criteria for both groups included: (<reflink idref="bib1" id="ref43">1</reflink>) acute alcohol intoxication, Wernicke's encephalopathy, alcohol withdrawal syndrome, epilepsy; (<reflink idref="bib2" id="ref44">2</reflink>) other substance dependence or use, co-occurring psychiatric disorders; (<reflink idref="bib3" id="ref45">3</reflink>) History of alcohol use, now abstinent; (<reflink idref="bib4" id="ref46">4</reflink>) aphasia, hearing impairments, severe somatic diseases, or any other condition that would interfere with fNIRS testing.</p> <p>The final sample included 123 participants in the alcohol dependence group and 149 in the healthy control group. The study was approved by the Ethics Committee of the First Hospital of Hebei Medical University (Approval No. 20220722). All subjects had informed consent to participate in this study. Patients with alcohol dependence received a 3-week benzodiazepine therapy during hospitalization, along with symptomatic treatment.</p> <hd id="AN0182974355-5">Study procedures</hd> <p></p> <hd id="AN0182974355-6">Demographic data</hd> <p>Demographic and drinking history data were collected from the electronic medical record system, including age, gender, education level, family history of drinking, past medical history, age of onset of drinking, duration of alcohol dependence, and daily alcohol consumption (standard drinks). Clinical diagnoses for all participants were made by psychiatrists using ICD-10 criteria.</p> <hd id="AN0182974355-7">Verbal Fluency Task (VFT)</hd> <p>All participants underwent a VFT [<reflink idref="bib32" id="ref47">32</reflink>],[<reflink idref="bib33" id="ref48">33</reflink>] prior to treatment to activate haemodynamic responses in different brain regions. In a quiet room, participants sat still in a chair with their heads immobilized and followed task instructions. The task included a 30-s rest period before the task, during which participants repeated "1.2.3.4.5" following the instructions. This was followed by a 60-s task period where participants were instructed to form as many words as possible using given Chinese characters such as "big," "sky," and "white." After the task period, there was another 30-s rest period with repetition of "1.2.3.4.5" until the end of the examination.</p> <hd id="AN0182974355-8">Functional Near-Infrared Spectroscopy (fNIRS) Measurement</hd> <p>fNIRS measurements were taken simultaneously with the VFT using a Hitachi ETG-4000 device. Near-infrared light with wavelengths of 690 nm and 830 nm was used to illuminate the cerebral cortex through a fiber-optic cap worn on the head. The cap included 16 receiving probes and 17 emitting probes, constituting 52 channels, and changes in the concentration of oxyhemoglobin and deoxyhemoglobin were measured [<reflink idref="bib13" id="ref49">13</reflink>]. Common technical indicators included the concentration of oxyhemoglobin, β-values, integral values, and centroid values [<reflink idref="bib34" id="ref50">34</reflink>]. The concentration of oxyhemoglobin is the most direct and commonly used indicator during fNIRS measurements, reflecting changes in cerebral blood flow dynamics during the task. The β-value, representing the degree of signal activation corresponding to actual changes in oxyhemoglobin concentration, was extracted using a General Linear Model (GLM) [<reflink idref="bib35" id="ref51">35</reflink>]. The integral value represents the area under the curve of the oxyhemoglobin concentration in the positive coordinate part of the blood oxygen waveform, indicating the intensity of brain region activity [<reflink idref="bib36" id="ref52">36</reflink>]. The centroid value represents the concentration of oxyhemoglobin at the time corresponding to half of the area under the positive curve during the entire VFT, reflecting the activity state of the brain region at a specific time [<reflink idref="bib37" id="ref53">37</reflink>]. In this study, a further division of brain regions was done based on the regions where the 52 channels were located, including prefrontal lobe (Ch13-19, Ch24-29, Ch34-40, Ch45-50), right temporal lobe (Ch22-23, Ch32-33, Ch43-44), left temporal lobe (Ch30-31, Ch41-42, Ch51-52), parietal lobe (Ch1-12, Ch20-21). See Fig. 1.</p> <p>Graph: Fig. 1 Diagram of the location of the channels in relation to brain regions. Note: prefrontal lobe (Ch13-19, Ch24-29, Ch34-40, Ch45-50), right temporal lobe (Ch22-23, Ch32-33, Ch43-44), left temporal lobe (Ch30-31, Ch41-42, Ch51-52), parietal lobe (Ch1-12, Ch20-21).</p> <hd id="AN0182974355-9">fNIRS signals pre-processing</hd> <p>The fNIRS raw data were pre-processed using the processing functions in the Matlab software Homer2 toolkit, including motion artefact detection, filtering, baseline correction and so on, and all signals were corrected. Responding to neuronal activity by monitoring changes in blood flow is inevitably affected by a number of influences, e.g. the heart is supplying oxygen to the whole body when it is beating, which in turn affects the monitoring of brain activity, and must therefore be eliminated in subsequent signal processing. Changes in optical density yield changes in oxyhaemoglobin and deoxyhaemoglobin concentrations (HbO2 and dHb) via a modified version of Beer-Lambert's law, thereby inferring neuronal activity in the brain.</p> <hd id="AN0182974355-10">Telephone follow-up</hd> <p>Three months post-treatment, a telephone follow-up was conducted to determine whether the patients with alcohol dependence had relapsed, whether they were working, and whether they had experienced any negative life events.</p> <p>It is confirmed that all methods were performed in accordance with relevant guidelines and regulations.</p> <hd id="AN0182974355-11">Statistical methods</hd> <p>SPSS 26.0 software was used for statistical analysis to compare demographic data between the two groups. Categorical data were presented as frequencies and percentages (%), and comparisons between groups were made using t test, chi-square tests or Fisher's exact test. Non-normally distributed measurement data were described using medians and quartiles (M [<emph>p</emph>25, <emph>p</emph>75]). The Homer2 toolkit of Matlab software was used to process the raw fNIRS data and calculate the integral and centre of gravity values. The difference in the activation level of 52 channels between the two groups, i.e., the β-value, which responds to the magnitude of the change in the concentration of oxyhaemoglobin, was extracted using GLM, and the characteristics of the fNIRS data were analysed and plotted as a topological map under VFT. The Manm-Whitney <emph>U</emph> test was used for between-group comparisons of the differences in integral values, centroid values and β-values between the two groups, Spearman's rank correlation analysis was done for the indicators of drinking and fNIRS, and binary logistic regression modelling was used for the analysis of the fNIRS channels affecting the behaviour of return to drinking within 3 months, and the predictions calculated by logistic regression models were further used as probability as a test variable of ROC curve (Receiver Operating Characteristic Curve), plotted the ROC curve, compared the AUC (Area under curve), and analysed the clinical value of the fNIRS multi-channel combination in predicting the relapse of drinking in patients with alcohol dependence.The significance level was set at α = 0.05 for two-sided tests.</p> <hd id="AN0182974355-12">Results</hd> <p></p> <hd id="AN0182974355-13">Demographic and clinical data of alcohol dependence and healthy control groups</hd> <p>Through the <emph>X</emph><sups>2</sups> test or t-test, There were statistically significant differences between the groups in terms of educational level, history of cardiovascular disease, family history of drinking and total of words (<emph>p</emph> < 0.005). No other significant differences were observed (<emph>p</emph> > 0.05). See Table 1 for detailed comparisons.</p> <hd id="AN0182974355-14">Hemodynamic activity of oxyhemoglobin in the frontal lobe during VFT</hd> <p>Using the Manm-Whitney U test, there was a statistically significant difference between the alcohol-dependent group and the control group in the prefrontal (H = 3815.50, <emph>p</emph> < 0.001) integral values during the VFT task period, which was lower in the alcohol-dependent group than in the control group. See Fig. 2A. However, the difference in centroid values between the two groups was not statistically significant in the prefrontal lobes (<emph>p</emph> > 0.05). See Fig. 2B. The difference in β-values of all other channels (Ch13-Ch19, Ch24-Ch29, Ch34-Ch40, Ch45-Ch50) in the frontal lobe was statistically significant (<emph>p</emph> < 0.005) between the two groups of patients, except for Ch13, which was activated at a lower level in the alcohol-dependent group. See Fig. 2C. In addition, by means of waveform graphs we can also observe changes in blood oxygen concentration in the prefrontal lobe. See Fig. 2D.</p> <p>Graph: Fig. 2 Comparison of oxyhemoglobin activity in the frontal lobe between groups. Note: (A) integral values, (B) centroid values, (C) β-values. (D) Example waveform plots of changes in prefrontal oxygen and haemoglobin concentrations in selected individuals from both groups of subjects. AlcD: Alcohol Dependence group, HC: Healthy Control group. HbO2: oxyhaemoglobin, dHb: deoxyhaemoglobin. ns: No statistical significance, *: p < 0.05, **: p < 0.01, ***: p < 0.001, ****: p < 0.0001. Ch: channel.</p> <hd id="AN0182974355-15">Hemodynamic activity of oxyhemoglobin in the temporal lobes During VFT</hd> <p>Using the Manm-Whitney U test, bilateral temporal lobe point values were lower in the alcohol-dependent group compared to the control group (H = 4430.00, p < 0.001) and the difference was statistically significant. See Fig. 3A. However, there was no significant difference in centroid values between the two groups (<emph>p</emph> > 0.05). See Fig. 3B<bold>.</bold> Considering that language has a dominant hemisphere, we further compared the betas of the temporal lobes separately by right and left temporal lobes.</p> <p>Graph: Fig. 3 Comparison of oxyhemoglobin activity in the temporal lobes between groups. Note: (A) integral values, (B) centroid values, (C) β-values in the right temporal lobe, (D) β-values in the left temporal lobe. AlcD: Alcohol Dependence group, HC: Healthy Control group. ns: No statistical significance, *: p < 0.05, **: p < 0.01, ***: p < 0.001. Ch: channel.</p> <p>There were significant differences in oxyhemoglobin activation levels between the alcohol dependence group and the control group in the right temporal lobe channels (Ch22, Ch23, Ch32, Ch33, Ch43, Ch44), with the alcohol dependence group showing lower β-values (<emph>p</emph> < 0.005). See Fig. 3C. Comparing the left temporal lobe channels (Ch30, Ch31, Ch41, Ch42, Ch51, Ch52), the alcohol dependence group had significantly lower β-values compared to the control group (<emph>p</emph> < 0.005). See Fig. 3D.</p> <hd id="AN0182974355-16">Comparison of parietal oxyhaemoglobin signalling activation during VFT</hd> <p>After the Manm-Whitney <emph>U</emph> test, there were significant differences in β-values for oxyhemoglobin activation levels in the parietal lobe channels Ch11 and Ch20 (<emph>p</emph> < 0.005), while there were no significant differences in the remaining parietal channels (<emph>p</emph> < 0.05). See Fig. 4.</p> <p>Graph: Fig. 4 Comparison of oxyhemoglobin activation levels in channels located in parietal lobe between groups. Note: AlcD: Alcohol Dependence group, HC: Healthy Control group. ns: No statistical significance, *: p < 0.05. Ch: channel.</p> <hd id="AN0182974355-17">Topographic map of hemodynamic signal activation in the cerebral cortex</hd> <p>Topographic maps were produced by GLM analysis (Fig. 5). The alcohol dependence group showed generally suppressed oxyhemoglobin activation levels across the cerebral cortex, with channels Ch3, Ch13-Ch15, Ch24-Ch27, Ch34-Ch36, Ch38, Ch40, Ch41, Ch43, Ch45-Ch47, Ch49-Ch51 showing activation signals, albeit at lower intensities. The suppression was particularly evident in the channels located in the frontal lobe region (Fig. 5A). In the control group, 38 channels (Ch7, Ch11, Ch13, Ch17-Ch19, Ch21-Ch23, Ch24-Ch29, Ch30-Ch33, Ch34-Ch36, Ch37-Ch40, Ch41-Ch44, Ch45-Ch52) showed good activation, with the remaining channels displaying varying degrees of activation, with increased oxyhemoglobin concentration primarily in the frontal and temporal lobe regions (Fig. 5B). Comparing the activation levels of oxyhemoglobin between the two groups, except for Ch13, the remaining channels in the frontal lobe showed significant differences, with clear differences in signal activation levels observed in the frontal and bilateral temporal lobe regions (Fig. 5C).</p> <p>Graph: Fig. 5 Topographic map of oxyhemoglobin signal activation in the cerebral cortex related to VFT. Note: (A) Activation situation of each channel in the Alcohol Dependence group (n = 123), with the color scale threshold closer to blue indicating lower activation levels; (B) activation situation of each channel in the Healthy Control group (n = 149), with the color scale threshold closer to red indicating higher activation levels; (C) comparison of oxyhemoglobin signal activation levels between groups for each channel, with the color scale threshold closer to the extreme values indicating greater differences, while colors closer to green indicate no significant difference between the two groups. Channels 1–52 represent their relative locations.</p> <hd id="AN0182974355-18">Comparison of demographic and drinking data between relapse and non-relapse groups</hd> <p>Based on telephone follow-up, within the alcohol dependence group, 79 patients relapsed within 3 months (relapse group), while 44 did not relapse (non-relapse group). There were no statistically significant differences in general data between the two groups (<emph>p</emph> > 0.05), as shown in Table 2.</p> <hd id="AN0182974355-19">Correlation analysis between fNIRS indicators and drinking situations</hd> <p>Spearman rank correlation analysis showed that the age of onset of drinking was positively correlated with the activation level of oxyhemoglobin in the right Broca's area channels 13 (r = 0.221, <emph>p</emph> = 0.014) and 24 (r = 0.256, <emph>p</emph> = 0.004), as well as channel 42 in the left Wernicke's area (r = 0.270, <emph>p</emph> = 0.003) and the integral value of the temporal lobe (r = 0.219, <emph>p</emph> = 0.015). There were no statistically significant correlations between daily drinking amount, duration of addiction, and any of the indicators (<emph>p</emph> > 0.05). See Fig. 6 for the correlation analysis.</p> <p>Graph: Fig. 6 Correlation analysis between fNIRS indicators and drinking situations. Note: *: p < 0.05, **: p < 0.01. Ch: channel.</p> <hd id="AN0182974355-20">Analysis of factors influencing relapse behavior within 3 months</hd> <p>Two logistic regression models were constructed to analyze the influencing factors. Model 1 included the β-values of each channel. Logistic regression results (OR: Odds Ratio) showed that channels 11 (OR = 19,023.5, <emph>p</emph> = 0.012) and 32 (OR = 360,553.4, <emph>p</emph> = 0.007) in the right Wernicke's area and channel 40 in the left Broca's area (OR = 7,705,087,762, <emph>p</emph> = 0.031) were positively correlated with relapse behavior within 3 months. Channels 33 in the posterior central gyrus (OR = 0.023, <emph>p</emph> = 0.05) and 47 in the superior frontal orbital gyrus (OR = 0, <emph>p</emph> = 0.04) were negatively correlated with relapse behavior. See Fig. 7A for the logistic regression model.</p> <p>Graph: Fig. 7 Binary logistic regression models for factors influencing relapse behavior. Note: (A) Model 1, including β-values of each channel. (B) Model 2, controlling for age of onset of drinking, daily drinking amount, and duration of addiction. OR: Odds Ratio (95% CI) is log-transformed, Ch: channel.</p> <p>Model 2 controlled for the age of onset of drinking, daily drinking amount, and duration of addiction, based on Model 1. After verifying the absence of multicollinearity through linear regression, logistic regression results showed that channels 11 (OR = 1,521,264.4, <emph>p</emph> = 0.047) and 32 (OR = 9,629,768.798, <emph>p</emph> = 0.005) in the superior temporal gyrus and channel 40 in the triangular part of the inferior frontal gyrus (OR = 7.71781E + 12, <emph>p</emph> = 0.019) were positively correlated with relapse behavior within 3 months. See Fig. 7B for the logistic regression model.</p> <hd id="AN0182974355-21">fNIRS prediction analysis for relapse behavior within 3 months</hd> <p>A logistic regression model was fitted to calculate the predicted probability of different brain regions by using the β-value of the joint multichannel as the independent variable and the endpoint of the return-to-drinking behavior within 3 months as the dependent variable, and the predicted probability of the different brain regions was used as the test variable for the ROC curves, and the return-to-drinking behavior within 3 months was used as the state variable. ROC curve analysis indicated that the area under the curve for the frontal lobe was 0.758 (<emph>p</emph> < 0.0001), with a sensitivity of 0.734, specificity of 0.727, Youden's index of 0.461, and an optimal cutoff value of 0.621. The area under the curve for the parietal lobe was 0.720 (<emph>p</emph> < 0.0001), with a sensitivity of 0.962, specificity of 0.409, Youden's index of 0.371, and an optimal cutoff value of 0.498. The area under the curve for the bilateral temporal lobes was 0.705(<emph>p</emph> = 0.0002), with a sensitivity of 0.873, specificity of 0.5, Youden's index of 0.373, and an optimal cutoff value of 0.574. The area under the curve for all channels was 0.951(<emph>p</emph> < 0.0001), with a sensitivity of 0.924, specificity of 0.886, Youden's index of 0.81, and an optimal cutoff value of 0.526. See Fig. 8 for the ROC curve analysis of fNIRS prediction for relapse within 3 months after alcohol dependence treatment.</p> <p>Graph: Fig. 8 ROC curve analysis of fNIRS Prediction for relapse within 3 months in different brain regions after alcohol dependence treatment. Note: ROC: Receiver Operating Characteristic Curve, AUC: Area Under the ROC Curve, the AUC value is also closer to 1, indicating that the prediction accuracy of the model is higher.</p> <hd id="AN0182974355-22">Discussion</hd> <p>Current research has demonstrated that alcohol-dependent patients suffer from deficits in cognitive, emotional and memory cortical function due to the chronic effects of alcohol. In addition to cognitive functions related to the processing of information in the frontotemporal lobe, alcohol-induced frontotemporal lobe deficits have been shown to reduce active control and response inhibition[<reflink idref="bib38" id="ref54">38</reflink>],[<reflink idref="bib39" id="ref55">39</reflink>]. Present study indirectly confirmed the inhibition of cortical activity by alcohol through hemodynamic analysis of fNIRS, especially in the frontal and bilateral temporal lobes during the VFT compared to healthy controls, without pronounced reduction in the parietal lobe and surrounding motor areas of the central sulcus. There has been limited research on alcohol dependence and fNIRS[<reflink idref="bib40" id="ref56">40</reflink>],[<reflink idref="bib41" id="ref57">41</reflink>], but our findings regarding hemodynamic changes in the frontal and temporal lobes are consistent with these previous structural MRI and fMRI studies, with some studies have shown patients with repetitive drinking exhibited reduced medial prefrontal cortical activation during decision-making and reductions in the gray matter of the frontal cortex (including the medial orbitofrontal cortex), anterior cingulate gyrus, and insula in patients with alcohol dependence[<reflink idref="bib24" id="ref58">24</reflink>],[<reflink idref="bib42" id="ref59">42</reflink>]. Hahn's research identified thinning of the cortical thickness in the left superior frontal gyrus and right orbitofrontal cortex, as well as a decrease in the surface volume of the right superior temporal gyrus, as potential markers for the diagnosis of alcohol dependence [<reflink idref="bib43" id="ref60">43</reflink>]. This suggests that neuronal activity in the frontotemporal lobe is worse than in the healthy population, and that there may be impaired cortical activity associated with the frontotemporal lobe that in turn affects cognitive functioning aspects such as executive function and verbal memory. Additionally, Harel et al. demonstrated that high-frequency (10 Hz) deep repetitive transcranial magnetic stimulation of the medial prefrontal cortex and anterior cingulate cortex significantly reduced self-reported alcohol craving in patients with alcohol dependence [<reflink idref="bib44" id="ref61">44</reflink>], highlighting the importance of the frontal lobe in the pathogenesis of alcohol dependence.</p> <p>Our correlational analysis results show that the age at which drinking began is positively correlated with the activation level of oxyhemoglobin in the left Wernicke's area and right Broca's area, as well as the integral value of the bilateral temporal lobes. The older the age of initiation of alcohol consumption, the better the haemodynamic activity of the bilateral temporal lobes was maintained, which is consistent with the comparison of the differences in the fNIRS indexes between the alcohol-dependent patients and the healthy population of the present study. At the same time, interesting changes in the language centres of the temporal lobes and surrounding brain regions were found by the VFT task used in this study. The language-dominant hemisphere for most people is the left hemisphere, with the right hemisphere being secondary. The Wernicke's area, responsible for language comprehension and semantic processing, maintains better language function with later exposure to alcohol. Conversely, the Broca's area, associated with language production and execution of semantic tasks, shows poorer activation in the right secondary hemisphere with earlier exposure to alcohol. This suggests that prolonged alcohol consumption may lead to more active function in the Broca's area of the dominant hemisphere, possibly due to less inhibition by alcohol or even a disinhibition effect due to alcohol's suppression of certain neural circuits in the cerebral cortex. This may be related to the fact that some alcohol users have increased speech volume but disorganised speech content after drinking. However, further mechanistic studies are needed to confirm this speculation.</p> <p>Logistic regression analysis regarding relapse in alcohol dependence suggests that the activation levels of oxyhemoglobin in the channels of the left Broca's area and right Wernicke's area are influencing factors for relapse [<reflink idref="bib45" id="ref62">45</reflink>], and their increase may promote the occurrence of relapse behavior in the short term. This is consistent with the results of the rank correlation analyses described above in this study. This may be related to the inhibition of neuronal activity in the Wernicke's area by alcohol, leading to compensatory activation in the corresponding area of the secondary hemisphere, i.e., the right Wernicke's area, resulting in increased neuronal activity [<reflink idref="bib27" id="ref63">27</reflink>]. Alcohol's less pronounced inhibition of the Broca's area in the dominant hemisphere may even lead to more active language motor function. Furthermore, poorer hemodynamic activity in the frontal lobe, indicating cognitive decline, is closely related to relapse in alcohol dependence, consistent with findings by Zois et al. that a reduction in the volume of the medial orbitofrontal cortex is associated with an increased risk of relapse in alcohol dependence [<reflink idref="bib24" id="ref64">24</reflink>]. Further ROC curve analysis of predictive probability values derived from logistics regression in this study found that pre-treatment infrared thermography in alcohol-dependent patients was able to assess the approximate outcome of whether patients were likely to alcohol relapse in the short term after treatment, with parietal and bilateral temporal lobe sensitivity being better, but specificity was suboptimal, and prefrontal lobe sensitivity and specificity could be better, but that a combination of all-channels was useful for assessing treatment resumption of drinking outcomes in the short term is optimal. This suggests that fNIRS has great potential in predicting the prediction of resumption of drinking outcomes in alcohol-dependent patients.</p> <p>Limitations of this study include: (<reflink idref="bib1" id="ref65">1</reflink>) Only male subjects were selected, limiting the generalizability to female populations. The number of women in our country who consume alcohol and meet the inclusion criteria for our study is extremely limited. Considering the large gender difference after enrolment, this may lead to bias in the statistical analysis, thus affecting the representativeness of the sample. In the future, it is necessary to include more women in the research studies, despite the smaller sample size. Increasing the frequency and duration of follow-ups can provide further insights; (<reflink idref="bib2" id="ref66">2</reflink>) The study did not further investigate the influence of psychosocial aspects such as personality traits, life events and family support on patients' relapse into drinking, which could potentially bias the information; (<reflink idref="bib3" id="ref67">3</reflink>) This study lacked neuropsychological tests to analyse the assessment of cognitive functioning of fNIRS in patients with alcohol dependence from multiple perspectives, and neuropsychological tests should be included in subsequent follow-up to assess the recovery of cognitive and social functioning in patients who return to drinking or do not return to drinking after treatment. (<reflink idref="bib4" id="ref68">4</reflink>) During the 3-month follow-up period, fNIRS was not used to detect brain function resulting in an inability to better predict relapse outcomes. future studies should expand the sample size and include fNIRS review during the follow-up period for relapse outcomes to further explore the role of fNIRS in predicting the risk of relapse in alcohol-dependent patients. (<reflink idref="bib5" id="ref69">5</reflink>) The fNIRS device we used is more oriented towards clinical applications with limited channel design. Future experiments should be conducted using fNIRS devices with more channels to enhance and validate our findings.</p> <p>In summary, firstly, the present study found decreased haemodynamics and reduced neuronal activity in the frontotemporal cerebral cortex of alcohol-dependent patients by fNIRS. Second, the degree of activation of fNIRS signalling in Broca's area and Wernicke's area under the VFT task may be correlated with the presence of relapsing drinking behavior. Thirdly, Compared to fMRI, fNIRS presents advantages in terms of portability, cost-effectiveness, and time resolution, with less stringent patient positioning requirements, making examinations easier to conduct. When integrated with the VFT task, fNIRS holds the potential to predict relapse outcomes in alcohol-dependent patients within 3 months before treatment, offering valuable guidance for clinical therapy and management. However, there is currently a scarcity of related research, and more evidence from fNIRS studies on the neural circuits [<reflink idref="bib46" id="ref70">46</reflink>] and brain network functions [<reflink idref="bib47" id="ref71">47</reflink>] associated with alcohol dependence is needed to further explore the mechanisms and characteristic markers of relapse in patients with alcohol dependence.</p> <p>Table 1 Comparison of demographic data between alcohol dependence group (n = 123) and healthy control group (n = 149).</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Alcohol dependence (n = 123)</p></th><th align="left"><p>Healthy control (n = 149)</p></th><th align="left"><p><italic>F/</italic> × <italic>2/t</italic> value</p></th><th align="left"><p><italic>p</italic>-Value</p></th></tr></thead><tbody><tr><td align="left"><p>Age (years), <italic>M</italic> (<italic>p</italic>25,<italic>p</italic>75)</p></td><td align="left"><p>38 (33, 47)</p></td><td align="left"><p>41 (34.5, 50)</p></td><td align="left"><p>– 1.830</p></td><td align="left"><p>0.067</p></td></tr><tr><td align="left"><p>Educational level</p></td><td align="left" /><td align="left" /><td align="left"><p>13.856</p></td><td align="left"><p>0.003</p></td></tr><tr><td align="left"><p> Illiterate and primary school, n (%)</p></td><td align="left"><p>11 (8.9)</p></td><td align="left"><p>12 (8.1)</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Junior high school, n (%)</p></td><td align="left"><p>59 (48.0)</p></td><td align="left"><p>46 (30.9)</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p> High school, n (%)</p></td><td align="left"><p>44 (35.8)</p></td><td align="left"><p>60 (40.3)</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p> University and above, n (%)</p></td><td align="left"><p>9 (7.3)</p></td><td align="left"><p>31 (20.8)</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" colspan="5"><p>Past medical history</p></td></tr><tr><td align="left"><p> Cardiovascular disease, Yes, n (%)</p></td><td align="left"><p>39 (31.7)</p></td><td align="left"><p>7 (4.7)</p></td><td align="left"><p>34.98</p></td><td align="left"><p> < 0.001</p></td></tr><tr><td align="left"><p> Family history of alcohol dependence, Yes, n (%)</p></td><td align="left"><p>11 (8.9)</p></td><td align="left"><p>0 (0.0)</p></td><td align="left"><p>13.887</p></td><td align="left"><p>0.001</p></td></tr><tr><td align="left"><p> Age of onset of drinking (years), <italic>M</italic> (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>20 (18, 25)</p></td><td align="left"><p>/</p></td><td align="left"><p>/</p></td><td align="left"><p>/</p></td></tr><tr><td align="left"><p> Duration of addiction (years), <italic>M</italic> (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>5 (2, 7)</p></td><td align="left"><p>/</p></td><td align="left"><p>/</p></td><td align="left"><p>/</p></td></tr><tr><td align="left"><p> Daily drinking amount (standard drinks), <italic>M</italic> (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>20 (10, 20)</p></td><td align="left"><p>/</p></td><td align="left"><p>/</p></td><td align="left"><p>/</p></td></tr><tr><td align="left"><p> Total of words, X ± S</p></td><td align="left"><p>5.130 ± 2.01</p></td><td align="left"><p>9.304 ± 3.23</p></td><td align="left"><p>13.036</p></td><td align="left"><p> < 0.001</p></td></tr></tbody></table> </ephtml> </p> <p>Note: M: Median, <emph>p</emph>25: 25th percentile, <emph>p</emph>75: 75th percentile.</p> <p>Table 2 Comparison of general data and drinking situations between relapse group (n = 79) and non-relapse group (n = 44) [M (<emph>p</emph>25, <emph>p</emph>75)].</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Relapse (n = 79)</p></th><th align="left"><p>Non-relapse (n = 44)</p></th><th align="left"><p><italic>H-</italic>value</p></th><th align="left"><p><italic>p</italic>-value</p></th></tr></thead><tbody><tr><td align="left"><p>Age (years), M (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>38 (33, 48)</p></td><td align="left"><p>37 (32.25, 47.75)</p></td><td align="left"><p>1677.5</p></td><td char="." align="char"><p>0.749</p></td></tr><tr><td align="left"><p>Educational level</p></td><td align="left" /><td align="left" /><td align="left"><p>1.034</p></td><td char="." align="char"><p>0.309</p></td></tr><tr><td align="left"><p> Junior high school and below, n (%)</p></td><td align="left"><p>47 (59.5)</p></td><td align="left"><p>22 (50.0)</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left"><p> Above junior high school, n (%)</p></td><td align="left"><p>32 (40.5)</p></td><td align="left"><p>22 (50.0)</p></td><td align="left" /><td char="." align="char" /></tr><tr><td align="left" colspan="5"><p>Past medical history</p></td></tr><tr><td align="left"><p> Cardiovascular disease, Yes, n (%)</p></td><td align="left"><p>24 (30.4)</p></td><td align="left"><p>15 (34.1)</p></td><td align="left"><p>0.180</p></td><td char="." align="char"><p>0.672</p></td></tr><tr><td align="left"><p> Age of onset of drinking (years), M (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>20 (18, 25)</p></td><td align="left"><p>20 (17.25, 25)</p></td><td align="left"><p>1719.5</p></td><td char="." align="char"><p>0.922</p></td></tr><tr><td align="left"><p> Duration of addiction (years), M (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>5 (2, 7)</p></td><td align="left"><p>4.5 (2, 8)</p></td><td align="left"><p>1702.5</p></td><td char="." align="char"><p>0.850</p></td></tr><tr><td align="left"><p> Daily drinking amount (standard drinks), M (<italic>p</italic>25, <italic>p</italic>75)</p></td><td align="left"><p>20 (10, 20)</p></td><td align="left"><p>20 (10, 20)</p></td><td align="left"><p>1533.0</p></td><td char="." align="char"><p>0.240</p></td></tr><tr><td align="left"><p> Negative life events in 3 months, Yes (%)</p></td><td align="left"><p>6 (7.6)</p></td><td align="left"><p>2 (4.5)</p></td><td align="left"><p>0.076</p></td><td char="." align="char"><p>0.783</p></td></tr><tr><td align="left"><p> Return to work in 3 months, Yes (%)</p></td><td align="left"><p>15 (19.0)</p></td><td align="left"><p>8 (18.2)</p></td><td align="left"><p>0.012</p></td><td char="." align="char"><p>0.913</p></td></tr></tbody></table> </ephtml> </p> <p>Note: M: Median, <emph>p</emph>25: 25th percentile, <emph>p</emph>75: 75th percentile.</p> <hd id="AN0182974355-23">Acknowledgements</hd> <p>The authors of this manuscript would like to thank all the researchers involved in the study. H. An and R. Wang contributed equally to this work.</p> <hd id="AN0182974355-24">Author contributions</hd> <p>Anqi Huang: Investigation, data analysis, data curation, writing the original draft. Ran Wang: Data curation, writing the original draft. Aiping Wen: Data analysis. Lin Xu: Investigation. Na Li: Investigation. Yuanyuan Gao: Investigation. Wenting Lu: Investigation. Shijie Guo: Investigation. Jincheng Wang: Investigation, review, and editing of the manuscript. Lan Wang: Conceptualization, funding acquisition, review, and editing of the manuscript.</p> <hd id="AN0182974355-25">Funding</hd> <p>This work was supported by the Hebei Medical Science Research Project (No. 20230160), the Science & Technology Program of Hebei (No. SG2021189), Spark scientific research project growth fund of First Hospital of Hebei Medical University(No. XH202208) and the government funded clinical medicine excellent talents training project of Hebei Province (No.ZF2023021). The funder had no role in conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.</p> <hd id="AN0182974355-26">Data availability</hd> <p>The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.</p> <hd id="AN0182974355-27">Competing interests</hd> <p>The authors declare no competing interests.</p> <hd id="AN0182974355-28">Ethics approval and consent to participate</hd> <p>All studies complied with STROBE guidelines. All studies confirm to the principles outlined in the Declaration of Helsinki and have been approved by the Ethics Committee of the First Hospital of Hebei Medical University (Approval No.20220722).</p> <hd id="AN0182974355-29">Consent for publication</hd> <p>All authors have read and agreed to the published version of the manuscript. Publication permission was granted by the First Hospital of Hebei Medical University.</p> <hd id="AN0182974355-30">Abbreviations</hd> <p></p> <p>• ADS</p> <p></p> <ulist> <item> Alcohol dependence Scale</item> <p></p> </ulist> <p>• AlcD</p> <p></p> <ulist> <item> Alcohol dependence</item> <p></p> </ulist> <p>• AUC</p> <p></p> <ulist> <item> Area under curve</item> <p></p> <item> Deoxy-Hb/dHb</item> <p></p> <item> Deoxyhemoglobin</item> <p></p> </ulist> <p>• EEG</p> <p></p> <ulist> <item> Electroencephalography</item> <p></p> </ulist> <p>• fMRI</p> <p></p> <ulist> <item> Functional magnetic resonance imaging</item> <p></p> </ulist> <p>• fNIRS</p> <p></p> <ulist> <item> Functional near-infrared spectroscopy</item> <p></p> </ulist> <p>• ICD-10</p> <p></p> <ulist> <item> International Statistical Classification of Diseases and Related Health Problems 10th Revision</item> <p></p> <item> Oxy-Hb/HbO2</item> <p></p> <item> Oxyhemoglobin</item> <p></p> </ulist> <p>• ROC</p> <p></p> <ulist> <item> Receiver operating characteristic curve</item> <p></p> </ulist> <p>• VFT</p> <p></p> <ulist> <item> Verbal fluency task</item> </ulist> <hd id="AN0182974355-31">Publisher's note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0182974355-32"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Schuckit MA. 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  Data: Clinical value of predicting relapse within 3 months in alcohol-dependent patients using fNIRS in verbal fluency task
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Anqi+Huang%22">Anqi Huang</searchLink><br /><searchLink fieldCode="AR" term="%22Ran+Wang%22">Ran Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Aiping+Wen%22">Aiping Wen</searchLink><br /><searchLink fieldCode="AR" term="%22Lin+Xu%22">Lin Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Na+Li%22">Na Li</searchLink><br /><searchLink fieldCode="AR" term="%22Yuanyuan+Gao%22">Yuanyuan Gao</searchLink><br /><searchLink fieldCode="AR" term="%22Wenting+Lu%22">Wenting Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Shijie+Guo%22">Shijie Guo</searchLink><br /><searchLink fieldCode="AR" term="%22Jincheng+Wang%22">Jincheng Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Lan+Wang%22">Lan Wang</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Scientific Reports, Vol 15, Iss 1, Pp 1-13 (2025)
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  Data: Nature Portfolio, 2025.
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  Data: 2025
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: LCC:Medicine<br />LCC:Science
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Alcohol+dependence%22">Alcohol dependence</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+near-infrared+spectroscopy%22">Functional near-infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Verbal+fluency+task%22">Verbal fluency task</searchLink><br /><searchLink fieldCode="DE" term="%22Relapse%22">Relapse</searchLink><br /><searchLink fieldCode="DE" term="%22Medicine%22">Medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Science%22">Science</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Abstract 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 0.05). 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.
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  Data: 2045-2322
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  Data: https://doaj.org/toc/2045-2322
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  Label: DOI
  Group: ID
  Data: 10.1038/s41598-025-89775-7
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        Value: 10.1038/s41598-025-89775-7
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1
    Subjects:
      – SubjectFull: Alcohol dependence
        Type: general
      – SubjectFull: Functional near-infrared spectroscopy
        Type: general
      – SubjectFull: Verbal fluency task
        Type: general
      – SubjectFull: Relapse
        Type: general
      – SubjectFull: Medicine
        Type: general
      – SubjectFull: Science
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      – TitleFull: Clinical value of predicting relapse within 3 months in alcohol-dependent patients using fNIRS in verbal fluency task
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