Brain Metabolic Patterns Research Articles

Acetyl-DL-leucine in two individuals with REM sleep behavior disorder improves symptoms, reverses loss of striatal dopamine-transporter binding and stabilizes pathological metabolic brain pattern—case reports

Isolated REM Sleep Behavior Disorder (iRBD) is considered a prodrome of Parkinson’s disease (PD). We investigate whether the potentially disease-modifying compound acetyl-DL-leucine (ADLL; 5 g/d) has an effect on prodromal PD progression in 2 iRBD-patients. Outcome parameters are RBD-severity sum-score (RBD-SS-3), dopamine-transporter single-photon emission computerized tomography (DAT-SPECT) and metabolic “Parkinson-Disease-related-Pattern (PDRP)”-z-score in 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). After 3 weeks ADLL-treatment, the RBD-SS-3 drops markedly in both patients and remains reduced for >18 months of ADLL-treatment. In patient 1 (female), the DAT-SPECT putaminal binding ratio (PBR) decreases in the 5 years pretreatment from normal (1.88) to pathological (1.22) and the patient’s FDG-PET-PDRP-z-score rises from 1.72 to 3.28 (pathological). After 22 months of ADLL-treatment, the DAT-SPECT-PBR increases to 1.67 and the FDG-PET-PDRP-z-score stabilizes at 3.18. Similar results are seen in patient 2 (male): his DAT-SPECT-PBR rises from a pretreatment value of 1.42 to 1.72 (close to normal) and the FDG-PET-PDRP-z-score decreases from 1.02 to 0.30 after 18 months of ADLL-treatment. These results support exploration of whether ADLL may have disease-modifying properties in prodromal PD.

Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum.

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.

PET-based brain molecular connectivity in neurodegenerative disease.

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach. Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity. Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Data-driven subtypes of mixed semantic-logopenic primary progressive aphasia: Linguistic features, biomarker profiles and brain metabolic patterns

Mixed primary progressive aphasia (mPPA) accounts for a substantial proportion of primary progressive aphasia (PPA) cases. However, the lack of a standardised definition of this condition has resulted in misclassification of PPA cases. In this study, we enrolled 55 patients diagnosed with PPA, comprising 12 semantic variant (svPPA), 23 logopenic variant (lvPPA), and 20 mPPA cases with linguistic characteristics consistent with both svPPA and lvPPA (s/lvPPA). All patients underwent language assessments, evaluation of Alzheimer's disease biomarkers (via cerebrospinal fluid analysis or Amyloid-PET), and 18F-FDG-PET brain scans. An agglomerative hierarchical clustering (AHC) analysis based on linguistic characteristics revealed two distinct clusters within the s/lvPPA group: cluster k1 (n = 10) displayed an AD-like biomarker profile, with lower levels of Aβ42 and Aβ42/Aβ40 ratio, along with higher levels of t-tau and p-tau compared to cluster k2 (n = 10). Interestingly, k1 exhibited linguistic features that were similar to those of svPPA. Both clusters exhibited extensive temporoparietal hypometabolism. These findings support the hypothesis that a subgroup of s/lvPPA may represent a clinical manifestation of AD-related PPA.

The diagnostic and prognostic value of tau-PET in amnestic MCI with different FDG-PET subtypes.

Mild cognitive impairment presenting with an amnestic syndrome (aMCI) and amyloid positivity is considered due to AD. Many subjects, however, can show an overall very slow progression relevant for differential diagnosis, prognosis, and treatment. This study assessed PET biomarkers, includingbrain glucose metabolism, tau, and amyloid load, in a series of comparable aMCI at baseline, clinically evaluated at follow-up. We included 72 aMCI subjects from Geneva Memory Center (N = 31) and ADNI cohorts (N = 41), selected based on available FDG-PET, tau-PET, amyloid-PET, and clinical follow-up (2.3 years ± 1.2). A data-driven algorithm classified brain metabolic patterns into subtypes that were then compared for clinical and PET biomarker measures and cognitive decline. Voxel-wise comparisons were performed both with FDG-PET and tau-PET data. The algorithm classified three metabolic subtypes, namely "Hippocampal-sparing with cortical hypometabolism" (Type1; N = 27), "Hippocampal and cortical hypometabolism" (Type 2; N = 23), and "Medial temporal hypometabolism" (Type 3; N = 22). Amyloid positivity and tau accumulation in the medial temporal and neocortical regions characterized Type 1 and Type 2, whereas Type 3 showed no significant tau pathology, variable amyloid positivity, and stability at follow-up. All tau-positive patients, independently of the FDG-based subtype, showed faster cognitive decline. aMCI subjects can differ in metabolic patterns, tau and amyloid pathology, and clinical progression. Here, we complemented with PET tau biomarker the specific brain hypometabolic patterns at the individual level in the prodromal phase, contributing to the patient's classification. Tau PET is the most accurate biomarker in supporting or excluding the AD diagnosis in aMCI across metabolic subtypes and also predicting the risk of decline.

Artificial Intelligence's Transformative Role in Illuminating Brain Function in Long COVID Patients Using PET/FDG.

Cutting-edge brain imaging techniques, particularly positron emission tomography with Fluorodeoxyglucose (PET/FDG), are being used in conjunction with Artificial Intelligence (AI) to shed light on the neurological symptoms associated with Long COVID. AI, particularly deep learning algorithms such as convolutional neural networks (CNN) and generative adversarial networks (GAN), plays a transformative role in analyzing PET scans, identifying subtle metabolic changes, and offering a more comprehensive understanding of Long COVID's impact on the brain. It aids in early detection of abnormal brain metabolism patterns, enabling personalized treatment plans. Moreover, AI assists in predicting the progression of neurological symptoms, refining patient care, and accelerating Long COVID research. It can uncover new insights, identify biomarkers, and streamline drug discovery. Additionally, the application of AI extends to non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), which have shown promise in alleviating Long COVID symptoms. AI can optimize treatment protocols by analyzing neuroimaging data, predicting individual responses, and automating adjustments in real time. While the potential benefits are vast, ethical considerations and data privacy must be rigorously addressed. The synergy of AI and PET scans in Long COVID research offers hope in understanding and mitigating the complexities of this condition.

Probable 4-Repeat Tauopathy Criteria Predict Brain Amyloid Negativity, Distinct Clinical Features, and FDG-PET/MRI Neurodegeneneration Patterns in Corticobasal Syndrome.

Corticobasal syndrome (CBS) is associated with diverse underlying pathologies, including the four-repeat (4R)-tauopathies. The Movement Disorders Society (MDS) criteria for progressive supranuclear palsy (PSP) proposed the novel category "probable 4R-tauopathy" to address the phenotypic overlap between PSP and corticobasal degeneration (CBD). To investigate the clinical ability of the MDS-PSP criteria for probable 4R-tauopathy in predicting a negative amyloid-PET in CBS. Additionally, this study aims to explore CBS patients classified as 4R-tauopathy concerning their clinical features and neuroimaging degeneration patterns. Thirty-two patients with probable CBS were prospectively evaluated and split into those who fulfilled or did not fulfill the 4R-tauopathy criteria (CBS-4RT+ vs. CBS-4RT-). All patients underwent positron emission tomographies (PET) with [18 F]fluorodeoxyglucose and [11 C]Pittsburgh Compound-B (PIB) on a hybrid PET-MRI scanner to perform multimodal quantitative comparisons with a control group. Eleven patients were clinically classified as CBS-4RT+, and only one had a positive PIB-PET. The CBS-4RT+ classification had 92% specificity, 52% sensitivity, and 69% accuracy in predicting a negative PIB-PET. The CBS-4RT+ group presented with dysarthria and perseveration more often than the CBS-4RT- group. Moreover, the CBS-4RT+ group showed a prominent frontal hypometabolism extending to the supplementary motor area and striatum, and brain atrophy at the anterior cingulate and bilateral striata. The 4R-tauopathy criteria were highly specific in predicting a negative amyloid-PET in CBS. Patients classified as 4R-tauopathy presented distinct clinical aspects, as well as brain metabolism and atrophy patterns previously associated with tauopathies.

High expression of Alzheimer’s disease‐related metabolic brain pattern is predictive of conversion to mild cognitive impairment or dementia and faster cognitive decline

AbstractBackgroundAlzheimer’s disease‐related pattern (ADRP) is a metabolic brain biomarker of Alzheimer’s disease (AD), which was previously identified and validated. Its utility for differential diagnosis is known. Here, we aimed to explore its predictive value for conversion from cognitively normal (CN) to mild cognitive impairment (MCI), from MCI to dementia and for the rate of cognitive decline.MethodsWe analysed 609 participants followed for up to 15 years and their FDG PET scans from ADNI database (CN = 133, MCI = 352, dementia due to AD = 124) and 56 participants from University Medical centre Ljubljana (MCI = 22, AD = 34). ADRP expression was calculated from pre‐processed FDG PET scans. CN and MCI participants were stratified according to their conversion status and cerebrospinal fluid biomarkers. Amyloid positivity (A+) was defined as Aβ42 < 880 pg/mL and tau positivity (T+) as pTau > 21.8 pg/mL. AD patients were stratified into slow (< 2 points/per year decline on mini mental state examination (MMSE)) and fast (> 2 points/per year decline on MMSE) progressors.ResultsA+T+ cCN had higher baseline ADRP expression than A–T– (p<0.001), A+T– (p = 0.003) and A+T+ (p = 0.01) stable CN corrected for age, while the groups did not differ in gender, education or baseline MMSE scores. A+T+ cMCI had higher baseline ADRP expression than A–T– (p<0.001), A+T– (p = 0.001) and A+T+ (p<0.001) stable MCI, corrected for baseline age and MMSE. A+T– stable MCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.02). ADRP was a predictor of conversion to dementia in survival analysis (HR = 4.5[2.4‐8.7]). Similarly, in the UMCL dataset A+T+ cMCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.03). Fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p<0.001). Similarly, in the UMCL dataset fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p = 0.02). In neither dataset, AD groups differed in age, sex or baseline MMSE scores.ConclusionADRP expression levels can be used to predict conversion from CN to MCI and from MCI to dementia in A+T+ individuals. AD patients with high ADRP expression are at an elevated risk for faster progression of cognitive decline.

High expression of Alzheimer’s disease‐related metabolic brain pattern is predictive of conversion to mild cognitive impairment or dementia and faster cognitive decline

AbstractBackgroundAlzheimer’s disease‐related pattern (ADRP) is a metabolic brain biomarker of Alzheimer’s disease (AD), which was previously identified and validated. Its utility for differential diagnosis is known. Here, we aimed to explore its predictive value for conversion from cognitively normal (CN) to mild cognitive impairment (MCI), from MCI to dementia and for the rate of cognitive decline.MethodWe analysed 609 participants followed for up to 15 years and their FDG PET scans from ADNI database (CN = 133, MCI = 352, dementia due to AD = 124) and 56 participants from University Medical centre Ljubljana (MCI = 22, AD = 34). ADRP expression was calculated from pre‐processed FDG PET scans. CN and MCI participants were stratified according to their conversion status and cerebrospinal fluid biomarkers. Amyloid positivity (A+) was defined as Aß42 < 880 pg/mL and tau positivity (T+) as pTau > 21.8 pg/mL. AD patients were stratified into slow (< 2 points/per year decline on mini mental state examination (MMSE)) and fast (> 2 points/per year decline on MMSE) progressors.ResultA+T+ cCN had higher baseline ADRP expression than A–T– (p<0.001), A+T– (p = 0.003) and A+T+ (p = 0.01) stable CN corrected for age, while the groups did not differ in gender, education or baseline MMSE scores. A+T+ cMCI had higher baseline ADRP expression than A–T– (p<0.001), A+T– (p = 0.001) and A+T+ (p<0.001) stable MCI, corrected for baseline age and MMSE. A+T– stable MCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.02). ADRP was a predictor of conversion to dementia in survival analysis (HR = 4.5[2.4‐8.7]). Similarly, in the UMCL dataset A+T+ cMCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.03). Fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p<0.001). Similarly, in the UMCL dataset fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p = 0.02). In neither dataset, AD groups differed in age, sex or baseline MMSE scores.ConclusionADRP expression levels can be used to predict conversion from CN to MCI and from MCI to dementia in A+T+ individuals. AD patients with high ADRP expression are at an elevated risk for faster progression of cognitive decline.

24 Functional Distinctions in Metabolic Network Patterns in Parkinson's Disease

Objective:Cognitive decline is a common non-motor feature of Parkinson's disease (PD). However, the underlying mechanisms of cognitive impairment in PD require further elucidation. FDG PET imaging data analyses have revealed distinct brain metabolic patterns associated with the cognitive features of PD. The PD cognition-related pattern (PDCP) and default mode network (DMN) are two overlapping, but topographically distinct, networks that may serve as biomarkers of cognitive decline in PD. Decreased activity of the resting-state DMN and increased expression of the PDCP are associated with cognitive impairment in PD. Studies have consistently demonstrated the association between neuropsychological memory test performance and PDCP expression. Thus, we examined whether memory performance could offer additional value in predicting PDCP expression in PD patients. We hypothesized that DMN and memory performance would predict greater variance in PDCP expression than the DMN alone.Participants and Methods:Participants included 48 PD patients ages 46-80 (mean (SD) Age: 61.9 (8.1), Education: 15.0 (2.8), IQ: 112.5 (14.9), DRS total: 136.7 (5.8)). All participants completed the FDG PET and neuropsychological evaluation 8-12 hours after their last dose of Levodopa. Neuropsychological memory testing included the California Verbal Learning Test (CVLT) z score of sum of learning trials. PDCP and DMN values were z scores generated from normal controls in previous studies. Data were analyzed using linear regression analyses.Results:A hierarchical regression was performed to predict PDCP as a function of DMN and CVLT learning performance. Variables were entered in two separate blocks. The first block included DMN as a predictor, and the overall regression was significant (R2 = 0.55, F(1, 39)= 47.0, p < 0.001). As hypothesized, DMN significantly predicted PCDP expression (β= -0.74, p < 0.001). The second block of the regression included CVLT learning memory performance. Both DMN and CVLT performance explained a significant amount of variance in PDCP (R2 change = 0.05, F(2, 39)= 27.6, p < 0.001). CVLT performance significantly predicted PDCP (β= -0.22, p =0.048). The final model accounted for 60.0% of the variance in PDCP.Conclusions:Disruptions in functional connectivity within brain networks have become increasingly recognized as mechanisms responsible for cognitive impairment in patients. As demonstrated in previous studies, our results indicated that DMN loss is a strong predictor of PDCP expression, likely due to the networks' overlapping spatial regions. However, we found that the addition of memory performance to the model could explain a small amount of variance (5%) over and above DMN expression. Overall, the current findings demonstrate a functional (i.e., learning) distinction between population-specific (PDCP) and more general brain networks (DMN).

The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma

BackgroundOwing to the advances in diagnosis and therapy, survival or remission rates for lymphoma have improved prominently. Apart from the lymphoma- and chemotherapy-related somatic symptom burden, increasing attention has been drawn to the health-related quality of life. The application of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has been routinely recommended for the staging and response assessment of FDG-avid lymphoma. However, up till now, only a few researches have investigated the brain metabolic impairments in patients with pre-treatment lymphoma. The determination of the lymphoma-related metabolic brain pattern would facilitate exploring the tailored therapeutic regimen to alleviate not only the physiological, but also the psychological symptoms. In this retrospective study, we aimed to establish the diffuse large B-cell lymphoma-related pattern (DLBCLRP) of metabolic brain network and investigate the correlations between DLBCLRP and several indexes of the staging and response assessment.ResultsThe established DLBCLRP was characterized by the increased metabolic activity in bilateral cerebellum, brainstem, thalamus, striatum, hippocampus, amygdala, parahippocampal gyrus and right middle temporal gyrus and by the decreased metabolic activity in bilateral occipital lobe, parietal lobe, anterior cingulate gyrus, midcingulate cortex and medial frontal gyrus. Significant difference in the baseline expression of DLBCLRP was found among complete metabolic response (CMR), partial metabolic response (PMR) and progressive metabolic disease (PMD) groups (P < 0.01). DLBCLRP expressions were also significantly or tended to be positively correlated with international prognostic index (IPI) (rs = 0.306, P < 0.05), lg(total metabolic tumor volume, TMTV) (r = 0.298, P < 0.05) and lg(total lesion glycolysis, TLG) (r = 0.233, P = 0.064). Though no significant correlation of DLBCLRP expression was found with Ann Arbor staging or tumor SUVmax (P > 0.05), the post-treatment declines of DLBCLRP expression were significantly positively correlated with Ann Arbor staging (rs = 0.284, P < 0.05) and IPI (rs = 0.297, P < 0.05).ConclusionsThe proposed DLBCLRP would lay the foundation for further investigating the cerebral dysfunction related to DLBCL itself and/or treatments. Besides, the expression of DLBCLRP was associated with the tumor burden of lymphoma, implying a potential biomarker for prognosis.

Abnormal brain glucose metabolism patterns in patients with advanced non-small-cell lung cancer after chemotherapy：A retrospective PET study

PurposeThis study was designed to investigate the acute or chronic post-chemotherapy effect and different chemotherapy cycles effect on brain glucose metabolism. MethodsA total of seventy-three patients who received chemotherapy after being diagnosed with advanced non-small-cell lung cancer (NSCLC) and underwent 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scan at Nuclear Medicine Department of the Fifth Hospital of Sun Yat-sen University between September 2017 and August 2022 were included. Seventy-two healthy control patients who underwent whole-body 18F-FDG PET/CT scans at our department, without any evidence of malignancy and confirmed by follow-up visits, were included. Advanced NSCLC patients were classified into six arms: short-to-long course (chemotherapy cycles under 4, between 5 and 8 and more than 8) in acute chemotherapy effect (AC) group (scanned 18F-FDG PET/CT within 6 months post-chemotherapy) or chronic chemotherapy effect (CC) group (the interval between scanning and the last chemotherapy session more than six months). Statistical Parametric Mapping (SPM) analysis between patients’ groups and healthy controls’ brain 18F-FDG PET was performed (uncorrected p ˂ 0.001 with cluster size above 20 contiguous voxels). ResultsThere were no significant differences between patients’ groups and healthy controls in age, gender and body mass index (BMI). SPM PET analyses revealed anomalous brain metabolic activity in different groups (p ˂ 0.001). Short-course + AC group exhibited hypermetabolism in the cerebellum and widespread hypometabolism in bilateral frontal lobe predominantly. Only hypometabolic brain regions were observed in middle-course + AC patients. Long-course + AC group displayed a greater number of abnormalities. Notably, these metabolic abnormalities tended to decrease in CC groups versus AC groups across all courses. ConclusionOur study revealed that patients with advanced NSCLC who underwent chemotherapy exhibited persistent abnormal brain metabolism patterns during continuous chemotherapy and these abnormalities tended to recover after completion of chemotherapy over time, but without correlation to an increasing number of chemotherapy cycles. 18F-FDG PET/CT may serve as a possible modality for evaluating brain function and guiding appropriate treatment timing.

Imaging Correlates between Headache and Breast Cancer: An [18F]FDG PET Study.

This study aimed to examine brain metabolic patterns on [18F]Fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) in breast cancer (BC), comparing patients with tension-type headache (TTH), migraine (MiG), and those without headache. Further association with BC response to neoadjuvant chemotherapy (NAC) was explored. In this prospective study, BC patients eligible for NAC performed total-body [18F]FDG PET/CT with a dedicated brain scan. A voxel-wise analysis (two-sample t-test) and a multiple regression model were used to compare brain metabolic patterns among TTH, MiG, and no-headache patients and to correlate them with clinical covariates. A single-subject analysis compared each patient's brain uptake before and after NAC with a healthy control group. Primary headache was diagnosed in 39/46 of BC patients (39% TTH and 46% MiG). TTH patients exhibited hypometabolism in specific brain regions before NAC. TTH patients with a pathological complete response (pCR) to NAC showed hypermetabolic brain regions in the anterior medial frontal cortex. The correlation between tumor uptake and brain metabolism varied before and after NAC, suggesting an inverse relationship. Additionally, the single-subject analysis revealed that hypometabolic brain regions were not present after NAC. Primary headache, especially MiG, was associated with a better response to NAC. These findings suggest complex interactions between BC, headache, and hormonal status, warranting further investigation in larger prospective cohorts.

Brain metabolic signatures in patients with genetic and nongenetic amyotrophic lateral sclerosis.

To study the brain metabolic signature in Chinese amyotrophic lateral sclerosis (ALS) patients and compare the difference in brain metabolic patterns between ALS with and without genetic variants. We included 146 patients with ALS and 128 healthy controls (HCs). All patients with ALS underwent genetic testing to screen for ALS related genetic variants and were then divided into genetic (n = 22) and nongenetic ALS (n = 93) subgroups. All participants underwent brain 18 F-FDG-PET scans. Group comparisons were performed using the two-sample t-test model of SPM12. We identified a large of hypometabolic clusters in ALS patients as compared with HCs, especially in the bilateral basal ganglia, midbrain, and cerebellum. Moreover, hypometabolism in the bilateral temporal lobe, precentral gyrus and hypermetabolism in the left anterior cingulate, occipital lobe, and bilateral frontal lobe were also found in ALS patients as compared with HCs. Compared with nongenetic ALS patients, genetic ALS patients showed hypometabolism in the right postcentral gyrus, precuneus, and middle occipital gyrus. The incidence of sensory disturbance in patients with genetic ALS was higher than that in patients with nongenetic ALS (5 of 22 [22.72%] vs. 7 of 93 [7.52%], p = 0.036). Our investigation provided unprecedented evidence of relative hypometabolism in the midbrain and cerebellum in ALS patients. Genetic ALS patients showed a specific signature of brain metabolism and a higher incidence of sensory disturbance, indicating that genetic factors may be an underlying cause affecting the brain metabolism and increasing the risk of sensory disturbance in ALS.

The Poly I:C maternal immune stimulation model shows unique patterns of brain metabolism, morphometry, and plasticity in female rats.

Introduction: Prenatal infections are associated with an increased risk of the onset of schizophrenia. Rodent models of maternal immune stimulation (MIS) have been extensively used in preclinical studies. However, many of these studies only include males, omitting pathophysiological features unique to females. The aim of this study is to characterize the MIS model in female rats using positron emission tomography (PET), structural magnetic resonance imaging (MR), and neuroplasticiy studies. Methods: In gestational day 15, Poly I:C (or Saline) was injected into pregnant Wistar rats to induce the MIS model. Imaging studies: [18F]-fluoro-2-deoxy-D-glucose-PET scans of female-offspring were acquired at post-natal day (PND) 35 and PND100. Furthermore, T2-MR brain images were acquired in adulthood. Differences in FDG uptake and morphometry between groups were assessed with SPM12 and Regions of Interest (ROI) analyses. Ex vivo study: The density of parvalbumin expressing interneurons (PV), perineuronal nets (PNN), and parvalbumin expressing interneurons surrounded by perineuronal nets (PV-PNN) were evaluated in the prelimbic cortex and basolateral amygdala using confocal microscopy. ROIs and neuroplasticity data were analyzed by 2-sample T-test and 2-way-ANOVA analyses, respectively. Results: A significant increase in brain metabolism was found in all animals at adulthood compared to adolescence. MIS hardly modified brain glucose metabolism in females, highlighting a significant hypometabolism in the thalamus at adulthood. In addition, MIS induced gray matter (GM) enlargements in the pituitary, hippocampus, substantia nigra, and cingulate cortex, and GM shrinkages in some thalamic nuclei, cerebelar areas, and brainstem. Moreover, MIS induced white matter shrinkages in the cerebellum, brainstem and corpus callosum, along with cerebrospinal fluid enlargements in the lateral and 4th ventricles. Finally, MIS reduced the density of PV, PNN, and PV-PNN in the basolateral amygdala. Conclusion: Our work showed in vivo the differential pattern of functional and morphometric affectation in the MIS model in females, as well as the deficits caused at the synaptic level according to sex. The differences obtained highlight the relevance of including both sexes in psychiatric research in order to consider their pathophysiological particularities and successfully extend the benefits obtained to the entire patient population.

Using illusions to understand hallucinations: differences in perceptual performances on illusory figures may underscore specific visuoperceptual impairments in Parkinson's disease.

Visual hallucinations are prevalent, potentially disabling symptoms of Parkinson's Disease. Multiple impairments in bottom-up sensory processing and top-down perceptual modulation are implicated in the pathophysiology of these phenomena. In healthy individuals, visual illusions are elicited by illusory figures through parametric manipulations of geometrical configurations, contrast, color, or spatial relationships between stimuli. These illusory percepts provide insight on the physiologic processes subserving conscious and unconscious perception. In this exploratory, cross-sectional, controlled study, perceptual performance on illusory figures was assessed on 11 PD patients with hallucinations, 10 non-hallucinating PD patients, and 10 age-matched healthy individuals. In order to characterize potential neural substrates of perceptual performances, patients' brain metabolic patterns on FDG PET were also analyzed. Illusions relying on attentional modulation and global perception were attenuated in PD patients without hallucinations. This pattern was no longer recognizable in hallucinating patients. Conversely, illusory effects normally counteracted by figure to background segregation and overlapping figures recognition were enhanced in PD patients with hallucinations. FDG PET findings further suggest that perceptual differences between PD patients might be linked to abnormal top-down perceptual modulation.

PET and SPECT Imaging of ALS: An Educational Review.

Amyotrophic lateral sclerosis (ALS) is a disease leading to progressive motor degeneration and ultimately death. It is a complex disease that can take a significantly long time to be diagnosed, as other similar pathological conditions must be ruled out for a definite diagnosis of ALS. Noninvasive imaging of ALS has shed light on disease pathology and altered biochemistry in the ALS brain. Other than magnetic resonance imaging (MRI), two types of functional imaging, positron emission tomography (PET) and single photon emission computed tomography (SPECT), have provided valuable data about what happens in the brain of ALS patients compared to healthy controls. PET imaging has revealed a specific pattern of brain metabolism through [18F]FDG, while other radiotracers have uncovered neuroinflammation, changes in neuronal density, and protein aggregation. SPECT imaging has shown a general decrease in regional cerebral blood flow (rCBF) in ALS patients. This educational review summarizes the current state of ALS imaging with various PET and SPECT radiopharmaceuticals to better understand the pathophysiology of ALS.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies

BackgroundBrain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies. MethodsWe included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson’s Disease (PD) normal cognition on long-term follow-up (PD – low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD – high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses (“gold standard”) as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA). ResultsThe visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941). ConclusionsSPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Parkinson’s Disease-Related Brain Metabolic Pattern Is Expressed in Schizophrenia Patients during Neuroleptic Drug-Induced Parkinsonism

Drug-induced parkinsonism (DIP) is a frequent parkinsonian syndrome that appears as a result of pharmacotherapy for the management of psychosis. It could substantially hamper treatment and therefore its diagnosis has a direct influence on treatment effectiveness. Although of such high importance, there is a lack of systematic research for developing neuroimaging-based criteria for DIP diagnostics for such patients. Therefore, the current study was aimed at applying a metabolic brain imaging approach using the 18F-FDG positron emission tomography and spatial covariance analysis to reveal possible candidates for DIP markers. As a result, we demonstrated, to our knowledge, the first attempt at the application of the Parkinson's Disease-Related Pattern (PDRP) as a metabolic signature of parkinsonism for the assessment of PDRP expression for schizophrenia patients with DIP. As a result, we observed significant differences in PDRP expression between the control group and the groups with PD and DIP patients. Similar differences in PDRP expression were also found when the non-DIP schizophrenia patients were compared with the PD group. Therefore, our findings made it possible to conclude that PDRP is a promising tool for the development of clinically relevant criteria for the estimation of the risk of developing DIP.

Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.

Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.