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Abstract Background The heterogeneity of cognitive impairments in schizophrenia has been widely observed. However, reliable cognitive boundaries to differentiate the subgroups remain elusive. The key challenge for cognitive subtyping is applying an integrated and standardized cognitive assessment and understanding the subgroup-specific neurobiological mechanisms. The present study endeavors to explore cognitive subgroups and identify their morphological features. Methods A total of 920 schizophrenia patients and 169 healthy controls were recruited. MATRICS Consensus Cognitive Battery was applied to assess cognitive performance and recognize cognitive subgroups through latent profile and latent transition analysis. Cortical thickness and gray matter volume were employed for the morphological features across subgroups. Results Four reproducible cognitive subgroups were identified, including multidomain-intact, executive-preserved, executive-deteriorated, and multidomain-deteriorated subgroup. After 12 weeks of follow-up, the cognitive characteristics of three out of the four subgroups kept stability, except for multidomain-deteriorated subgroup in which 48.8% of patients with improved cognition transited into the executive-deteriorated subgroup. Across subgroups, significant gradient features of brain structure were exhibited in fronto-temporal regions, hippocampus, and insula. Compared to healthy controls, multidomain-intact subgroup showed the most intact cognition and morphology, and multidomain-deteriorated subgroup with youngest age showed morphological decline in extensive regions. The remaining two subgroups showed intermediate cognitive performance, but could be distinguished by executive function and morphological differences in posterior cingulate cortex. Conclusions Our study provides novel insights into the heterogeneity of cognitive impairments in schizophrenia and the morphological features from cross-sectional and longitudinal levels, which could advance our understanding of complex cognition-morphology relationships and guide personalized interventions.

Abstract Background Although symptoms of depressive episodes in patients with bipolar depressive episodes (BDE) and major depressive disorder (MDD) are similar, the treatment strategies for these disorders are completely different, suggesting that BDE and MDD have different neurobiological backgrounds. In this study, we examined the relationship between brain function and clinical symptoms, particularly cognitive function, in female individuals with bipolar disorder and MDD experiencing depressive episodes. Methods Regional homogeneity (ReHo) was analyzed in 51 medication-free female patients with BDE, 63 medication-free female patients with MDD, and 45 female healthy controls (HCs). Depressive symptom severity was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24), and multidimensional cognitive function was evaluated using the MATRICS Consensus Cognition Battery. Partial correlation analysis was used to explore the links between the brain regions and clinical characteristics. A support vector machine (SVM) was used to assess the classification accuracy. Results Compared with HCs, patients with BDE and MDD had decreased ReHo in the left lobule VI of the cerebellum and increased ReHo in the left precuneus. Patients with BDE also had reduced ReHo in the left lobules IV–V of the cerebellum and increased ReHo in the right putamen, unlike patients with MDD who had no significant differences in these regions. Patients with BDE exhibited more severe cognitive deficits in processing speed, attention, word learning, and overall cognitive function than those with MDD. In patients with BDE, a significant negative correlation was found between the right putamen and HAMD-24 scores. However, no significant association was observed between abnormal ReHo levels and cognitive function. The SVM effectively differentiated between patients with BDE, MDD, and HCs. Conclusion Cognitive impairment was more severe in female patients with BDE than in those with MDD. Changes in the ReHo values of the right putamen and left lobules IV–V may serve as unique neuroimaging markers for BDE. Alterations in the ReHo values of the left precuneus and left lobule VI could serve as common pathophysiological mechanisms for BDE and MDD in women and indicate depressive states.

Background and hypothesis: Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ. Study design: 55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Naïve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ. Study results: The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90 %) comparing with AAL and YEO in the most diagnostic tasks. Conclusions: These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.

Background The research findings on the topological properties of functional connectomes (TP-FCs) in patients with schizophrenia (SZPs) exhibit inconsistencies and contradictions, which can be attributed to limitations such as small sample sizes and heterogeneous data processing techniques.Aims To address these limitations, we conducted a large-scale study. Uniform data processing flows were employed to investigate the aberrant TP-FCs and the associations between TP-FCs and symptoms or cognitions (A-TP-SCs) in SZPs.Methods The large-scale study included six datasets from four sites, involving 497 SZPs and 374 healthy controls (HCs). A uniform process for imaging data preprocessing and functional connectivity matrix configuration was used. ComBat was employed for data harmonisation, and various TPs were calculated. We explored between-group differences in brain functional integration (FI) and functional segregation (FS) measured with TP-FCs, and conducted partial correlation analyses, with adjustments for age, gender and educational level, to identify A-TP-SCs.Results Compared with random networks and HCs, SZPs maintained small-worldness and global FI capacity despite their compromised global FS capacity and resilience. A decline in nodal FI and FS capacity was observed in sensory areas, whereas an increase in nodal FI capacity was found in regions associated with cognition and information integration. In addition, associations between TP-FCs and positive symptoms, negative symptoms or cognitive functions including speed of processing, visual learning and the ability to inhibit cognitive interference were identified in SZPs.Conclusions The identified A-TP-SCs verified that reductions in FS and resilience indicated pathological impairments in schizophrenia. The A-TP-SCs or TP-FCs, which measured the same attributes of the functional connectomes, exhibited high internal consistency, robustly reinforcing these findings.

Abstract Background It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. Methods This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a

BackgroundCognitive subtypes of schizophrenia may exhibit different neurobiological characteristics. This study aimed to reveal the underlying neurobiological features between cognitive subtypes in the early course of schizophrenia (ECS). According to prior studies, we hypothesized to identify 2–4 distinct cognitive subtypes. We further hypothesized that the subtype with relatively poorer cognitive function might have lower brain spontaneous neural activity than the subtype with relatively better cognitive function.MethodCognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Resting-state functional magnetic resonance imaging scanning was conducted for each individual. There were 155 ECS individuals and 97 healthy controls (HCs) included in the subsequent analysis. Latent profile analysis (LPA) was used to identify the cognitive subtypes in ECS individuals, and amplitude of low-frequency fluctuations (ALFFs) was used to measure brain spontaneous neural activity in ECS individuals and HCs.ResultsLPA identified two cognitive subtypes in ECS individuals, containing a severely impaired subtype (SI, n = 63) and a moderately impaired subtype (MI, n = 92). Compared to HCs, ECS individuals exhibited significantly increased ALFF in the left caudate and bilateral thalamus and decreased ALFF in the bilateral medial prefrontal cortex and bilateral posterior cingulate cortex/precuneus (PCC/PCu). In ECS cognitive subtypes, SI showed significantly higher ALFF in the left precentral gyrus (PreCG) and lower ALFF in the left PCC/PCu than MI. Furthermore, ALFFs of left PreCG were negatively correlated with several MCCB cognitive domains in ECS individuals, while ALFF of left PCC/PCu presented opposite correlations.ConclusionOur findings suggest that differences in the brain spontaneous neural activity of PreCG and PCC/PCu might be the potential neurobiological features of the cognitive subtypes in ECS, which may deepen our understanding of the role of PreCG and PCC/PCu in the pathogenesis of cognitive impairment in schizophrenia.

Schizophrenia is a severe mental disorder affecting around 0.5–1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients’ age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.

BACKGROUND Major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression).METHODS Genetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis.RESULTS Genetic correlations of PTSD with the depressive phenotypes were in the range of 0.71–0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21–0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD.CONCLUSION Evidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology.FUNDING The National Key Research and Development Program of China and the National Natural Science Foundation of China.

Attractin (ATRN) is a widely expressed glycoprotein that is involved in energy homeostasis, neurodevelopment, and immune response. It is encoded by a gene spanning 180 kb on chromosome 20p13, a region previously implicated in schizophrenia by linkage studies. To address a possible role of ATRN in disorders of the central nervous system, we created an atrn knockout zebrafish line and performed behavioral tests. Adult atrn–/– zebrafish exhibited more pronounced attack behavior relative to wild-type control zebrafish in a tracking analysis. Biochemical analysis revealed elevated testosterone levels in atrn–/– zebrafish. At the gene expression level, we noted an upregulation of cyp51 and hsd17b7, key proteins in testosterone synthesis in the brains of both adult and larvae of atrn–/– zebrafish. In order to further elucidate the relationship between testosterone and behavioral syndromes, we then compared testosterone levels of 9,008 psychiatric patients and 247 healthy controls from the same catchment area. Of all subjects examined, male subjects with schizophrenia exhibited lower testosterone levels compared with controls. In contrast, female subjects with a diagnosis of schizophrenia or bipolar disorder featured higher testosterone levels than did same sex controls. Purposeful sampling of extreme groups showed reduced ATRN expression in a subset of these subjects. Finally, we identified 14 subjects with ATRN mutations. All of whom displayed abnormal testosterone levels. In summary, the interplay of ATRN and testosterone may help to explain sexual dimorphisms in selected behavioral phenotypes.

Emotional singing can affect vocal performance and the audience’s engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing. Recently, virtual reality (VR) technologies have been applied in several fields for training purposes. In this empirical comparative study, a VR training task was implemented to elicit emotions from singers and further assist them with improving their emotional singing performance. The VR training method was compared against the traditional self-imagination method. By conducting a two-stage experiment, the two methods were compared in terms of emotions’ elicitation and emotional singing performance. In the first stage, electroencephalographic (EEG) data were collected from the subjects. In the second stage, self-rating reports and third-party teachers’ evaluations were collected. The EEG data were analyzed by adopting the max-relevance and min-redundancy algorithm for feature selection and the support vector machine (SVM) for emotion recognition. Based on the results of EEG emotion classification and subjective scale, VR can better elicit the positive, neutral, and negative emotional states from the singers than not using this technology (i.e., self-imagination). Furthermore, due to the improvement of emotional activation, VR brings the improvement of singing performance. The VR hence appears to be an effective approach that may improve and complement the available vocal music teaching methods.

Background: Schizophrenia, regarded as a neurodevelopmental disorder, is characterized by positive symptoms, negative symptoms, and cognitive dysfunction. Investigating the spontaneous brain activity in patients with schizophrenia can help us understand the underlying pathophysiologic mechanism of schizophrenia. However, results concerning abnormal neural activities and their correlations with cognitive dysfunction/psychopathology of patients with schizophrenia were inconsistent.Methods: We recruited 57 first-diagnosed and drug-naive patients with schizophrenia and 50 matched healthy controls underwent magnetic resonance imaging. The Positive and Negative Syndrome Scale (PANSS) and the MATRICS Consensus Cognitive Battery were used to assess the psychopathology/cognitive dysfunction. Regional homogeneity (ReHo) was used to explore neural activities. Correlation analyses were calculated between abnormal ReHo values and PANSS scores/standardized cognitive scores. Lastly, support vector machine analyses were conducted to evaluate the accuracy of abnormal ReHo values in distinguishing patients with schizophrenia from healthy controls.Results: Patients with schizophrenia showed cognitive dysfunction, and increased ReHo values in the right gyrus rectus, right inferior frontal gyrus/insula and left inferior frontal gyrus/insula compared with those of healthy controls. The ReHo values in the right inferior frontal gyrus/insula were positively correlated with negative symptom scores and negatively correlated with Hopkins verbal learning test-revised/verbal learning. Our results showed that the combination of increased ReHo values in the left inferior frontal gyrus/insula and right gyrus rectus had 78.5% (84/107) accuracy, 85.96% (49/57) sensitivity, and 70.00% specificity, which were higher than other combinations.Conclusions: Hyperactivities were primarily located in the prefrontal regions, and increased ReHo values in the right inferior frontal gyrus/insula might reflect the severity of negative symptoms and verbal learning abilities. The combined increases of ReHo values in these regions might be an underlying biomarker in differentiating patients with schizophrenia from healthy controls.

Abstract Background Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level. Methods 24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features. Results The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia. Conclusions This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.

A functional brain network has attracted much attention due to its capability of characterizing the functional connectivity patterns of the brain. The existing methods for network construction usually rely on the conventional measures such as Pearson correlation to determine the pairwise similarity between each pair of brain regions, thus ignoring the global structure relationship and the information communication flow among different brain regions. To this end, this paper proposes a directional brain network construction method based on effective distance. Specifically, the effective distance can capture the hidden relevance pattern among all the brain regions and provide a directional functional network reflecting information propagation paths among the functional brain areas simultaneously. When estimating the probability of the direction and the strength of the connectivity between two regions, the structure information characterized by their neighbors is also considered in our method. The functional brain network produced by our method is more flexible in uncovering physiological mechanisms of the brain compared with the conventional undirected network. The experiments on two fMRI data analysis tasks, i.e., disease diagnosis and cognitive state detection, show that our method outperforms the conventional functional brain network approaches, including Pearson correlation, structural equation modeling, and sparse representation-based method.

Brain functional connectivity network (BFCN) analysis has been widely used in the diagnosis of mental disorders, such as schizophrenia. In BFCN methods, brain network construction is one of the core tasks due to its great influence on the diagnosis result. Most of the existing BFCN construction methods only consider the first-order relationship existing in each pair of brain regions and ignore the useful high-order information, including multi-region correlation in the whole brain. Some early schizophrenia patients have subtle changes in brain function networks, which cannot be detected in conventional BFCN construction methods. It is well-known that the high-order method is usually more sensitive to the subtle changes in signal than the low-order method. To exploit high-order information among brain regions, we define the triplet correlation among three brain regions, and derive the second-order brain network based on the connectivity difference and ordinal information in each triplet. For making full use of the complementary information in different brain networks, we proposed a hybrid approach to fuse the first- and second-order brain networks. The proposed method is applied to identify the biomarkers of schizophrenia. The experimental results on six schizophrenia datasets (totally including 439 patients and 426 controls) show that the proposed method outperforms the existing brain network methods in the diagnosis of schizophrenia.

Background: Patients with treatment-resistant schizophrenia (TRS) and non-treatment-resistant schizophrenia (NTRS) respond to antipsychotic drugs differently. Previous studies demonstrated that patients with TRS or NTRS exhibited abnormal neural activity in different brain regions. Accordingly, in the present study, we tested the hypothesis that a regional homogeneity (ReHo) approach could be used to distinguish between patients with TRS and NTRS.Methods: A total of 17 patients with TRS, 17 patients with NTRS, and 29 healthy controls (HCs) matched in sex, age, and education levels were recruited to undergo resting-state functional magnetic resonance imaging (RS-fMRI). ReHo was used to process the data. ANCOVA followed by post-hoc t-tests, receiver operating characteristic curves (ROC), and correlation analyses were applied for the data analysis.Results: ANCOVA analysis revealed widespread differences in ReHo among the three groups in the occipital, frontal, temporal, and parietal lobes. ROC results indicated that the optimal sensitivity and specificity of the ReHo values in the left postcentral gyrus, left inferior frontal gyrus/triangular part, and right fusiform could differentiate TRS from NTRS, TRS from HCs, and NTRS from HCs were 94.12 and 82.35%, 100 and 86.21%, and 82.35 and 93.10%, respectively. No correlation was found between abnormal ReHo and clinical symptoms in patients with TRS or NTRS.Conclusions: TRS and NTRS shared most brain regions with abnormal neural activity. Abnormal ReHo values in certain brain regions might be applied to differentiate TRS from NTRS, TRS from HC, and NTRS from HC with high sensitivity and specificity.

Hexanucleotide (GGGGCC) repeat expansion in C9ORF72 (HRE) causes frontotemporal lobar degeneration, frontotemporal dementia-amyotrophic lateral sclerosis, and amyotrophic lateral sclerosis. HRE was also seen in the genomes of patients suffering from several other degenerative diseases. However, whether it is present in the treatment-resistant schizophrenia patients remains unknown. Genotyping 386 patients suffering from treatment-resistant schizophrenia using the method of Repeat-Primed PCR, we reported here that no HRE was detected in the patients of Chinese Han.

Pyrazines are important flavor components and healthy active components in Baijiu, which including tetramethylpyrazine (TTMP). During the brewing process, the traceability of microbial communities and the content distribution characteristics of TTMP are important for improving the quality and style characteristics of compound-flavored Baijiu (CFB). However, the traceability analysis of microorganisms in fermented grains (FG)—used in the production of CFB—lacks quantitative and systematic evaluation. In this study, the microbial communities and TTMP content of Jiuqu (JQ), Liangpei (LP), FG, and pit mud (CP) used in CFB production were characterized; further, coordinate and discriminant analyses were employed to determine differences in microbial communities. Additionally, traceability and correlation analyses were performed to reveal the origin of microbial communities in FG. The source, content, and distribution characteristics of TTMP based on the brewing process have also been discussed. The results showed that most of the bacterial and fungal communities at different levels of FG came from other sources, and the microorganisms of Cladosporium, Acetobacter, Aspergillus, Methanosarcina, and Bacillus were considered have a osculating correlations with TTMP content of FG. Taken together, this study provides insights into the origin of microbial communities in FG and the distribution characteristics of TTMP based on the CFB brewing process. The current findings are conducive for optimizing the fermentation process and improving the quality and style characteristics of CFB.

ABSTRACTHigh-throughput sequencing was used to elucidate the microbial community of strong-flavor Daqu (SFD) (surface and central parts) produced in different Chinese regions, moisture and other six kinds of quality indicators of SFD were detected. In addition, Spearman correlation coefficient was used to identify the relationships between microbial community and quality indicators. In different sample groups of surface and central parts, the differential fungal genus was Alternaria, whereas differential bacteria genera were Weissella, Pediococcus and Leuconostoc. Spearman analysis showed that fungal genera Rhizopus, Stemphylium, and Alternaria were significantly positively correlated with saccharifying ability (p

IntroductionWhile the variation in physicochemical parameters, microbial communities, metabolism, composition, and the proportion of volatile components in fermented grains (FG) affect final Baijiu quality, their complex interactions during the ultra-long fermentation of compound-flavor Baijiu (CFB) are still poorly understood.MethodsIn this study, amplicon sequencing was used to analyze the microbial community, and headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC–MS) was used to analyze the volatile components in FG during ultra-long fermentation of CFB. The relationships between the dominant microbial communities, physicochemical parameters, and volatile components were analyzed using redundancy analysis and network analysis.ResultsDuring ultra-long fermentation, bacterial diversity was initially higher than during the mid and late stages. Fungal diversity in the mid stages was higher than that initially and later in the process. A total of 88 volatile components, including six alcohols, 43 esters, eight aldehydes and ketones, 13 acids, and 18 other compounds were detected in FG. Starch and reducing sugars in FG strongly affected the composition and function of bacterial and fungal communities. However, acidity had little effect on the composition and function of the bacterial flora. Lactobacillus, Bacillus, Weissella, and Pichia were the core microbial genera involved in metabolizing the volatile components of FG.DiscussionWe provide insights into the relationships and influences among the dominant microbial communities, physicochemical parameters, and volatile components during ultra-long fermentation of CFB. These insights help clarify the fermentation mechanisms of solid-state fermentation Baijiu (SFB) and control and improve the aroma quality of CFB.

The microbial composition and volatile components of fermented grains (FG) and pit mud (PM) are crucial for the quality and flavor of compound-flavor baijiu (CFB). The physicochemical indices, culturable microorganisms, microbial communities, and volatile components of FG and PM were analyzed and correlated in our research. Considering FG and PM, amplicon sequencing was used to analyze the microbial community and the volatile components were detected by headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME). For FG, redundancy analysis and correlation perfume Circos were used to clarify the correlations between the dominant microbial community and volatile components. The results showed that Aspergillus, Pichia, and Rhizopus were the main fungal microflora in FG and PM, whereas Lactobacillus and Bacillus were the dominant bacteria in FG, and Methanosarcina and Clostridium sensu stricto 12 were the dominant bacteria in the PM. The microbial community and volatile compounds in the CB sampled from the bottom layers of the FG were greatly affected by those in the PM. There were 32 common volatile components in CB and PM. For FG, most of the volatile components were highly correlated with Lactobacillus, Bacillus, Aspergillus, Pichia, and Monascus, which includes alcohols, acids and esters. This study reveals correlations between microbial composition, volatile components, and the interplay of FG and PM, which are conducive to optimizing the fermentation process and improving the quality of CFB base.