Background Iron accumulation in the brain varies substantially across regions and can influence MR relaxation properties. In forensic imaging, post-mortem MRI is used to assess tissue integrity, support neuropathological evaluations, and complement death investigations. However, the sensitivity of the longitudinal relaxation rate R1 (1/T1) to iron content in post-mortem tissue remains unclear. This study examines the relationship between regional iron concentration and R1 in unfixed human brains. Methods Thirteen post-mortem human brains (mean age = 65.9 ± 10.2 years) were scanned in situ at 3 T using a 3D inversion-recovery turbo spin echo sequence. R1 values were estimated from multi-TI fitting. Following MRI, brains were extracted and tissue samples were collected from basal ganglia, cortical regions, and white matter—areas commonly examined in forensic neuropathology. Iron concentration (mg/kg wet tissue) was quantified using inductively coupled plasma mass spectrometry (ICP-MS). Statistical analyses included region-wise and subject-level linear regressions, as well as mixed-effects models to incorporate inter-individual variability. Results Iron concentration was highest in the basal ganglia, followed by white matter and cortex. Significant positive associations between iron concentration and R1 were observed in the basal ganglia and cortex, while white matter showed no significant relationship. Mixed-effects models confirmed these associations but indicated that inter-subject variability accounted for most of the explained variance. Conclusions These findings indicate that R1 is only weakly related to iron concentration in post-mortem brain tissue. This limits its value as an iron-specific marker for forensic MRI assessments. Additional tissue properties—such as myelin content, water loss, and iron binding state—likely contribute to R1 variation and should be considered when interpreting post-mortem MRI investigations.

Cognitive task performance relies on functional adaptations in brain network organization. While much research has focused on functional connectivity, less is known about how metabolic relationships contribute to cognition. The emerging concept of metabolic connectivity allows to investigate metabolic networks; however, this has not been assessed during task engagement. This study investigates both metabolic and functional adaptations on cognitive demands, highlighting the complementary insights gained from integrating these modalities. We simultaneously acquired functional PET/MRI data in 49 participants performing a cognitive task (Tetris®) at two difficulty levels. Euclidean-similarity metabolic and functional connectivity matrices were estimated during resting-state and both task conditions separately. Brain network reconfigurations were analyzed using network-based statistics. We then assessed how functional and metabolic adaptations, independently and in combination, relate to task performance. Although overlapping patterns emerged in task-relevant regions, metabolic and functional reconfigurations also revealed distinct characteristics. The dorsal attention network emerged as a key metabolic node, while the default mode network reconfigured its functional connectivity to meet task demands. Importantly, the multimodal approach outperformed single modalities in predictive task performance. These findings highlight PET-based connectivity as a valuable tool for investigating task-related brain network dynamics, offering new insights into the metabolic underpinnings of cognitive function.

Background Visual word recognition relies on a finely tuned interplay between the ventral and dorsal visual pathways. While the ventral stream is classically regarded as the primary substrate for orthographic processing, converging evidence suggests that the dorsal stream may provide compensatory support under increased perceptual demands. In particular, the degree of orthographic transparency influences the relative involvement of these pathways. We conducted a functional MRI study in Italian, a transparent orthography, to investigate supportive neural recruitment during reading under perceptual degradation. Participants performed a word recognition task in which visual real words were degraded through four types of visual manipulation (rotation, mirroring, reduced contrast, increased letter spacing) to increase processing difficulty. Both whole-brain and region-of-interest analyses were performed. Results Analyses revealed robust engagement of canonical ventral reading regions across conditions, alongside increased recruitment of dorsal stream areas during degraded word processing, and this dorsal involvement scaled with increasing level of visual degradation. This dorsal involvement was most prominent in the superior parietal and temporo-parietal cortices, consistent with their role in visuo-spatial attention Conclusion These findings suggest that, even in transparent languages where phonological decoding is relatively straightforward, the dorsal pathway can be part of a flexible reorganisation of the reading system. By demonstrating stimulus-driven adaptation of the reading network, our results provide novel insights into the neural flexibility underlying visual word recognition and highlight the importance of dorsal-ventral interactions in sustaining reading performance under suboptimal perceptual conditions.

The brain's functional activity is shaped by the complex architecture of its fibers. Yet, the lack of a direct one-to-one mapping between functional and structural connections makes this relationship elusive. To date, most studies on structure-function coupling (SFC) have conceptualized function in terms of resting-state functional Magnetic Resonance Imaging (fMRI) connectivity. Here, we extend this framework to neurophysiological data by examining how magnetoencephalography (MEG) activity relates to the structural connectome, leveraging its rich spectral content and direct sensitivity to neuronal population dynamics. We show that the decoupling of MEG activity from structure is strongly associated with the expression levels of synaptic plasticity markers, pointing to a link between flexible functional reconfiguration and the molecular mechanisms of plasticity. Moreover, regions with greater decoupling exhibit higher neurotransmitter receptor diversity, underscoring neuromodulatory heterogeneity as a substrate for functional flexibility. This association is especially pronounced for slow-acting metabotropic receptors, whose diffuse and prolonged signaling may facilitate functional reorganization atop the structural connectome.

Neural activity encompasses both rhythmic oscillations and aperiodic background dynamics, reflecting complex brain function beyond traditional rhythm-centric views. Theaperiodiccomponent, once considered noise, is now recognised as a meaningful signal indicative of excitation-inhibition balance and intrinsic neural timescales. Here, we review advanced signal processing frameworks, including spectral parameterisation and burst detection algorithms, that disentangle theseperiodicandaperiodiccomponents. We critically evaluate evidence suggesting thataperiodicparameters track neurodevelopment and serve as candidate biomarkers for Alzheimer's Disease and Parkinsonism. Furthermore, we highlight how neuroengineering interventions, such as deep brain stimulation and acupuncture, actively modulate these features. Crucially, we address the current methodological heterogeneity in the field, proposing a standardisedroadmapfor estimation to resolve conflicting interpretations. These findings underscore the complementary roles of oscillatory and aperiodic dynamics, offering novel avenues for closed-loop brain-computer interfaces and personalized neurotherapeutics.

Within the Alzheimer disease (AD) spectrum, metabolic alterations occur in addition to proteinopathies. While hypometabolism is frequently observed in the late symptomatic stages, characterizing the metabolic changes during early AD stages might aid in both understanding its pathophysiology and in patient selection for future treatments. In this study, we conduct an exploratory, data-driven analysis aimed at better understanding the timing and specificity of the metabolic changes in early AD. Using kinetic analysis of [18F]FDG PET brain imaging data coming from 223 individuals, including 33 preclinical individuals and 19 symptomatic individuals, we find new evidence of a more complex spatiotemporal trajectory of glucose metabolism in early AD, which includes a paradoxical regional increase in glucose phosphorylation during preclinical AD. These findings suggest that the pathologic phases of AD parallel changes in brain glucose metabolism, which is readily assessable with [18F]FDG PET imaging. Moreover, they may indicate that metabolic interventions may work differently during preclinical AD as compared to the early symptomatic phase.

Brain-age models based on functional connectivity have shown promise for characterizing large-scale age-related network changes and advancing understanding of neural aging mechanisms. However, existing approaches still face challenges in achieving both robust predictive performance and stable identification of connectivity signatures. To address this, we developed a brain-age modeling framework that integrates SHAP-guided PCA back-projection to link model predictions to stable, interpretable connectivity signatures and aging trajectories. Resting-state fMRI data from 599 adults (age range 36–95 years) were used to compute whole-brain functional connectivity. An Orthogonal Auxiliary Guidance Convolutional Autoencoder (OAG-CAE) was developed to learn age-relevant latent representations while separating age-residual variation, followed by a regressor for brain-age prediction. To achieve stable connection-level interpretability, we designed a SHAP-guided PCA back-projection module that applies SHAP to PCA-reduced FC features, propagates attribution through the latent representation and predictive pathway, and projects PCA-level attributions back to connectivity space to localize age-relevant circuits. The brain-age model achieved robust accuracy across mid- to late adulthood (MAE = 6.308 ± 0.566 years; R^2 = 0.702 ± 0.043). SHAP analysis revealed systematic patterns of functional reorganization with aging. Negative SHAP-identified connectivity was concentrated within and between the somatomotor, ventral attention, and default mode networks, as well as in basal ganglia–thalamus–cortical pathways. Notably, a substantial proportion of these connections exhibited an inverted U trajectory peaking around age 70, including within-network effects in the somatomotor and default mode networks and cross-network thalamo-cortical and somatomotor–ventral attention links. In contrast, positive SHAP-identified connectivity emerged mainly in cerebellar links with the default mode, frontoparietal, and dorsal attention networks, as well as default mode–visual and dorsal attention–thalamic pathways. By integrating OAG-CAE-based latent representation learning with SHAP-guided PCA back-projection, this study establishes an interpretable brain-age modeling framework that links prediction to specific connectivity circuits. The identified signatures reveal stage-dependent reorganization across large-scale networks, including non-linear transitions during mid- to late adulthood. These findings offer new insight into large-scale functional brain aging and provide a structured basis for future investigations in pathological aging and independent cohorts.

Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [18F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants. We uncover a systematic relationship between long-range temporal correlations, quantified via the Hurst exponent, and glucose metabolism. Our findings reveal that persistent temporal dependencies impose a measurable metabolic cost, with brains exhibiting higher long-range temporal correlations incurring greater energetic demands. Beyond glucose metabolism, we also show that these dynamics are likely supported by continuous biosynthetic processes, such as protein synthesis, which are critical for neural circuit maintenance and remodeling. Overall, our results suggest that a significant fraction of the brain's so-called "Dark Energy" is actively spent to power spontaneous long-range temporal correlations.

Cerebral glucose metabolism and cortical morphology are known to undergo significant changes across the lifespan, yet their network-level coordination remains poorly understood. This study aimed to investigate whether individual-level metabolic connectivity (MC) reflects underlying inter-areal morphometric similarity, and to determine how this metabolic–morphometric coupling evolves across the adult lifespan. Dynamic [18F]FDG-PET and structural MRI data were acquired from 67 healthy adults (age range: 38–86 years). Individual MC networks were estimated based on the similarity between regional time–activity curves. Corresponding structural similarity networks were generated using the morphometric inverse divergence (MIND) framework, which integrates multiple vertex-wise features of cortical morphology. The correspondence between metabolic and structural networks was quantified at both global and local scales using Spearman correlations. General linear models were employed to assess age-related effects on MC–MIND similarity. MC demonstrated a robust positive association with cortical morphometric similarity (ρ = 0.32, p < 0.0001), an association that persisted after distance correction and was replicated at the individual level. Regional coupling followed a topographic gradient, peaking in heteromodal association cortices and reaching its minimum in paralimbic areas. Crucially, morphology–metabolism alignment systematically strengthened with age at the global level (β = 0.59, p < 0.001). Local age-related increases were spatially heterogeneous, predominantly affecting visual, dorsal parietal, and premotor cortices alongside adjacent multimodal regions. Individual-level MC captures the morphometric organisation of the brain. The age-related increase in morphology–metabolism coupling indicates that metabolic coordination becomes progressively more aligned with cortical architecture, consistent with reduced neuroenergetic flexibility in the ageing brain.

Cerebellar posterior lobes regulate various cognitive functions, and their damage can result in Cerebellar Cognitive Affective Syndrome (CCAS). The validity of the CCAS Scale (CCAS-S), a reliable tool to diagnose CCAS, remains unexplored in multiple sclerosis (MS). To evaluate the performance of CCAS-S in patients with MS (pwMS) at clinical onset, and to assess MRI characteristics of pwMS with CCAS (CCAS+) compared with healthy controls (HC) and with pwMS classified by standard cognitive assessments as cognitively impaired (CI+) or cognitively normal (CI− CCAS−). One-hundred three early pwMS underwent CCAS-S, Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS), and Delis-Kaplan Executive Function System Sorting Test (D-KEFS-ST), used to classify patients into CI− CCAS−, CI+, and CCAS+. Twenty HC and 66 patients also underwent MRI to obtain lesion and volumetric parameters, diffusion MRI metrics, and cerebellum-brain functional connectivity (FC) on resting-state functional MRI, which were compared between groups. CCAS-S identified 14 (14

Background, By integrating dynamic imaging with kinetic modeling, Total-Body PET (TBP) scanners enable quantitative characterization of molecular activity across multiple organs simultaneously. Prior to the introduction of TBP, PET kinetic modeling typically consisted of applying a predefined compartmental model within a volume of interest, which is assumed to exhibit homogenous kinetic behavior. However, the significant physiological heterogeneity among organs within the body underscores the need for alternative modeling strategies.Methods, This study compares three distinct modeling approaches for parametric mapping of [18F]FDG TBP imaging: 1) spectral analysis (SA), which enables the kinetic modeling of dynamic PET data without the need to specify the number of kinetic compartments or the way they exchange material with each other; 2) linearized graphical methods, which rely on simplifications of the full model structure that are valid within limited time windows, and include Patlak for late-phase kinetics and a one-tissue one-kinetic parameter (1T1K) model for early-phase kinetics; 3) conventional compartmental modeling, based on Weighted Non-Linear Least Squares (WNLLS), where the model configuration was selected statistically from a pre-defined set of compartmental model structures. The methods were evaluated on 60-minute dynamic scans from 5 healthy individuals, acquired using the uEXPLORER scanner.Results, All methods generated maps of comparable quality, with a consistent representation of the heterogeneity across organs (Pearson’s correlation between whole-body maps: 0.79 - 0.99). When focusing on the main organs of interest, the agreement was high between SA and WNLLS (linear regression R2: 0.89 - 0.99), but lower between graphical methods and WNLLS (linear regression R2: 0.61 - 0.78), with the largest differences observed in the heart. SA proved to be a good tradeoff between applicative flexibility and biological informativity. Nonetheless, appropriate strategies to mitigate overfitting must be adopted to ensure reliable application of SA.

Although functional connectomics typically relies on resting-state fMRI, its analytical methods have been applied to task fMRI data in the investigation of broader involvements of brain regions even if inactive during a specific task. The purpose of this study is to assess the feasibility of inferring a true resting-state connectivity from task-fMRI data and to investigate the impact of connectomic-based analysis on behavioral trait studies. To this purpose, subjects underwent two visual fMRI tasks. The Blood-Oxygen-Level-Dependent (BOLD) time-series were processed to get both a "task" condition and a "pseudo-resting" condition applying different task regression setups to derive connectomes. Stimulus-classification experiments were conducted to compare "task" and "pseudo-resting" connectomes. Additionally, the influence of task regression was assessed through a classification experiment comparing children with Developmental Dyslexia (DD) and Typical Readers (TR). While task regression successfully removes task-related content from fMRI signals, stimulus information could still be inferred from connectomes, regardless of the preprocessing method used. Furthermore, a Support Vector Machine (SVM) experiment effectively discriminates between DD and TR in both "task" and "pseudo-resting" conditions. The study explored the impact of preprocessing in task fMRI experiments analyzed with connectomics. The ability to classify the stimuli in "pseudo-resting" conditions suggests that connectomes retain task-related signals even after task regression. Discriminative connections vary across tasks, affecting how classifiers differentiate between DD and TR. Despite these task-related differences, preprocessing had no effect on the inference of classification rules, indicating that key features are similarly evaluated in both tasks.

Linked to motor control, cerebellum is increasingly recognized for its role in cognition and neurodegenerative disorders. This retrospective study investigates associations between cerebellar volumes and cognitive screening tools—the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA)—in individuals with mild cognitive impairment (MCI) or mild dementia. MoCA scores showed significant positive correlations with cognitive-related cerebellar regions, particularly the left Crus I lobule (r = 0.40, p = 0.02) and total Crus I volume (r = 0.36, p = 0.04). Regression analysis confirmed associations with the left Crus I (β = 0.08, p = 0.02) and right VIIB lobule (β = 0.033, p = 0.032), while MMSE scores correlated only with right Lobule X thickness (r = -0.35, p = 0.04). These findings suggest MoCA may better detect cerebellar-related cognitive impairments, underscoring the importance of including cerebellar evaluation in the early diagnosis of dementia.

Brain connectivity, quantified with diffusion MRI (structural connectivity, SC) and resting-state functional MRI (functional connectivity, FC), can offer crucial insights into glioma-brain network interactions. Currently, no standardized approach exists to integrate information from FC and SC and to identify potential tumor-induced abnormalities at the single-patient level. Variational autoencoders (VAEs) have been shown to be promising for learning the distribution of features representing a healthy brain and deviations thereof and can naturally be applicable to multiple modalities. This study explores the potential of VAE to integrate FC and SC and detect multimodal anomalies in brain connectivity in glioma patients. The VAE is trained on concatenated FC-SC healthy data to learn how to reconstruct normative connectivity patterns. After ad hoc transfer learning, the model parameters are applied to the oncological dataset, to obtain the healthy version of the pathological matrices. Given the healthy, pathological, and reconstructed matrices, a statistic is developed with the goal of identifying specific alterations in SC, FC, and their FC + SC integration in glioma patients. SC, FC, and FC + SC abnormalities are compared with each other to explore their interplay and their link with tumor and surrounding brain tissues. Results show that FC is more sensitive to alterations distant from the tumor, while SC is more affected in its vicinity. Then, the alterations identified by FC are generally more in agreement with the alterations identified by FC + SC compared with those highlighted by SC. Moreover, SC abnormalities never overlap with FC + SC out of the tumor, and FC and SC single impairments partially overlap within the tumor core and never overlie in other brain tissues. This information could facilitate patient stratification, prognostic modeling, and personalized treatment planning.

Abstract Objective Fetal brain magnetic resonance imaging (MRI) provides insights into the architecture of the human brain. Recently, an increasing interest has been posed on transient brain structures, such as the ganglionic eminence (GE), to better understand potential derailments or anomalies in neurodevelopment. In this work, we define a spatio-temporal atlas of the GE from 19 to 36 gestational weeks (GW) in a 0.5-mm isotropic resolution. Materials and methods We extended the T2-weighted developing Human Connectome Project atlas with 19 and 20 GW and generated GE label maps spanning 19–36 GW. The GE label maps were generated via an averaging ensemble strategy of the segmentations performed by three expert neuroradiologists. Results The segmentations conducted by the experts achieved 0.91 ± 0.06 Dice similarity coefficient throughout the whole range of GW, indicating a strong agreement in this task. The GE reached its maximum volume expansion at around 21 GW, followed by a pronounced reduction throughout pregnancy (R 2 = 0.98, ranged 40‒500 mm3), highlighting an inverse relationship to the whole brain volume and cortical gray matter. This is accompanied by an increased number of small and fragmented components, correlating with known dynamics of GE migration toward target structures. Conclusion The proposed spatio-temporal GE MRI atlas supports the monitoring during pregnancy of this fascinating brain structure. It may aid in better understanding prodromic signs of potential future clinical conditions attributable to GE alterations. Moreover, it could be used as a repository of knowledge to develop innovative atlas-based deep learning models for biometric, volumetric, and shape analysis. Relevance statement The spatio-temporal fetal MRI atlas of the GE allows researchers to study its evolution and potential future clinical conditions attributable to GE alterations in pregnancy. The GE reached its maximum volume expansion around 21 GW, followed by a pronounced reduction throughout the pregnancy. Key Points The development of GE is a resource for monitoring pregnancy. We propose a spatio-temporal GE MRI atlas from 19 to 36 weeks of gestation. The GE reached its maximum expansion at around 21 weeks of gestation, followed by a progressive decline throughout pregnancy. Graphical Abstract

Glioblastoma is a malignant primary brain tumor. Because of its highly invasive and infiltrative nature, surgical resection and radiation therapy are not able to remove all tumor cells, even with state-of-the-art imaging and fluorescence-guided surgery. 24 newly diagnosed glioblastoma patients were enrolled. Pre- and post-surgery MRI scans were performed. Magnetic susceptibility was quantified based on gradient echo MRI. The ratio between sub-voxel paramagnetic and diamagnetic susceptibility components was computed. Relationships between the proposed ratio metric and prognostic factors and pathological iron were investigated. Perfusion and permeability imaging were used to exclude the presence of blood-related contribution to the paramagnetic component. Here we show that by decomposing tissue magnetic susceptibility into paramagnetic and diamagnetic sources, we can identify, non-invasively and in vivo, areas of altered iron metabolism associated with tumor activities in the edema tissue surrounding glioblastoma. We find that the paramagnetic to diamagnetic susceptibility ratio uniquely delineates area of hyperintensity corresponding to a Tumor and Immune cells Infiltration Zone. Statistically significant relationships are found between the ratio metrics in the infiltration zone and tumor prognostic factors. Follow-up scans reveal tumor progression and later contrast-enhancement in the predicted infiltration zone. Histological data indicate that increased iron content causes the elevated ratio metric. Our study proposes a method to derive an iron-related imaging marker of abnormal patterns in the edema region of the glioblastoma associated with tumor cell infiltration. We show the potential of the imaging marker to aid and improve surgical and treatment planning. Debiasi et al. examine glioblastoma in the edema region of the tumor, by computing the tissue magnetic properties using MRI data to develop a method to automatically identify areas indicative of infiltration. Association is demonstrated between magnetic properties and tumor prognostic factors, which is caused by an elevated iron presence in the tumor tissues. Glioblastoma is a brain tumor with very infiltrative behavior. Current treatment options are not able to remove it in its entirety. Consequently, residual tumor is found in most patients after surgery, causing early recurrence and decreased survival. We focused on the edema region of the tumor, usually homogeneous on conventional MRI, to identify potential areas of infiltration. By computing the tissue magnetic properties using MRI data, we observed abnormal spatial patterns in edema. We developed a method to automatically identify these abnormal areas. We found an association between the magnetic properties of those areas and tumor prognostic factors, which is caused by an elevated iron presence in the tumor tissues. We further showed that this method may be used to track tumor infiltration. Our method could be readily employed in clinical practice to aid surgical resection and treatment planning.

The brain’s resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [ 18 F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [ 18 F]FDG kinetic parameters K i (irreversible uptake), K 1 (delivery), k 3 (phosphorylation) in a large healthy control group (n = 47). Describing the parameters’ spatial distribution at high resolution (216 regions), we showed that K 1 is the least redundant (strong posteromedial pattern), and K i and k 3 have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [ 18 F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO 2 ) from 15 O PET. Rs-fMRI-only models explained part of the individual variance in K i (35%), K 1 (14%), k 3 (21%), while combining rs-fMRI and CMRO 2 led to satisfactory description of K i (46%) especially. K i was sensitive to both local rs-fMRI variables ( ReHo ) and CMRO 2 , k 3 to ReHo , K 1 to CMRO 2 . This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.

Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-calledmolecular connectivityapproach that captures relationships of molecular imaging measures in different brain regions. Targeting these inter-regional interactions within a neuroreceptor system may allow to better understand complex brain functions. In this article, we provide a comprehensive review of molecular connectivity studies in the field of neurotransmission. We examine the expanding use of molecular connectivity approaches, highlighting their applications, advantages over traditional methods, and contributions to advancing neuroscientific knowledge. A systematic search in three bibliographic databases MEDLINE, EMBASE, and Scopus on July 14, 2023 was conducted. A second search was rerun on April 4, 2024. Molecular imaging studies examining functional interactions across brain regions were included based on predefined inclusion and exclusion criteria. Thirty-nine studies were included in the scoping review. Studies were categorised based on the primary neurotransmitter system being targeted: dopamine, serotonin, opioid, muscarinic, glutamate, and synaptic density. The most investigated system was the dopaminergic and the most investigated disease was Parkinson's disease (PD). This review highlighted the diverse applications and methodologies in molecular connectivity research, particularly for neurodegenerative diseases and psychiatric disorders. Molecular connectivity research offers significant advantages over traditional methods, providing deeper insights into brain function and disease mechanisms. As the field continues to evolve, embracing these advanced methodologies will be essential to understand the complexities of the human brain and improve the robustness and applicability of research findings in clinical settings.