Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer's disease

Alzheimer＇s disease （AD） is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging （structural magnetic resonance imaging （MRI）, diffusion MRI, and functional MRI） and neurophysiological techniques （electroencephalography and magnetoencephaJography） with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment （MCI）, the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks （i.e., connectomics） and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.

Whole-Brain Connectome of GABAergic Neurons in the Mouse Zona Incerta

The zona incerta(ZI)is involved in various functions and may serve as an integrative node of the circuits for global behavioral modulation.However,the long-range connectivity of different sectors in the mouse ZI has not been comprehensively mapped.Here,we obtained whole-brain images of the input and output connections via fluorescence micro-optical sectioning tomography and viral tracing.The principal regions in the input-output circuits of ZI GABAergic neurons were topologically organized.The 3D distribution of cortical inputs showed rostro-caudal correspondence with different ZI sectors,while the projection fibers from ZI sectors were longitudinally organized in the superior colliculus.Clustering results show that the medial and lateral ZI are two different major functional compartments,and they can be further divided into more subdomains based on projection and input connectivity.This study provides a comprehensive anatomical foundation for understanding how the ZI is involved in integrating different information,conveying motivational states,and modulating global behaviors.

The Brain Connectome for Chinese Reading

Chinese,as a logographic language,fundamentally differs from alphabetic languages like English.Previous neuroimaging studies have mainly focused on alphabetic languages,while the exploration of Chinese reading is still an emerging and fast-growing research field.Recently,a growing number of neuroimaging studies have explored the neural circuit of Chinese reading.Here,we summarize previous research on Chinese reading from a connectomic perspective.Converging evidence indicates that the left middle frontal gyrus is a specialized hub region that connects the ventral with dorsal pathways for Chinese reading.Notably,the orthography-to-phonology and orthography-to-semantics mapping,mainly processed in the ventral pathway,are more specific during Chinese reading.Besides,in addition to the left-lateralized language-related regions,reading pathways in the right hemisphere also play an important role in Chinese reading.Throughout,we comprehensively review prior findings and emphasize several challenging issues to be explored in future work.

Frequency-Resolved Connectome Hubs and Their Test-Retest Reliability in the Resting Human Brain

Functional hubs with disproportionately extensive connectivities play a crucial role in global information integration in human brain networks.However,most resting-state functional magnetic resonance imaging(R-fMRI)studies have identified functional hubs by examining spontaneous fluctuations of the blood oxygen level-dependent signal within a typical low-frequency band(e.g.,0.01–0.08 Hz or 0.01–0.1 Hz).Little is known about how the spatial distributions of functional hubs depend on frequency bands of interest.Here,we used repeatedly measured R-fMRI data from 53 healthy young adults and a degree centrality analysis to identify voxelwise frequency-resolved functional hubs and further examined their test-retest reliability across two sessions.We showed that a wide-range frequency band(0.01–0.24 Hz)accessible with a typical sampling rate(fsample=0.5 Hz)could be classified into three frequency bands with distinct patterns,namely,low-frequency(LF,0.01–0.06 Hz),middle-frequency(MF,0.06–0.16 Hz),and high-frequency(HF,0.16–0.24 Hz)bands.The functional hubs were mainly located in the medial and lateral frontal and parietal cortices in the LF band,and in the medial prefrontal cortex,superior temporal gyrus,parahippocampal gyrus,amygdala,and several cerebellar regions in the MF and HF bands.These hub regions exhibited fair to good test-retest reliability,regardless of the frequency band.The presence of the three frequency bands was well replicated using an independent R-fMRI dataset from 45 healthy young adults.Our findings demonstrate reliable frequency-resolved functional connectivity hubs in three categories,thus providing insights into the frequency-specific connectome organization in healthy and disordered brains.

The structural connectome in ADHD

Attention-deficit/hyperactivity disorder(ADHD)has been conceptualized as a brain dysconnectivity disorder.In the past decade,noninvasive diffusion magnetic resonance imaging(dMRI)studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome,and that these alterations are associated with core symptoms and cognitive deficits in patients.This review aims to summarize recent dMRI-based structural connectome studies in ADHD from voxel-,tractography-,and network-based perspectives.Voxel-and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts,the corpus callosum,the corticospinal tracts,and the cingulum bundle in patients with ADHD.Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes.The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification.These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation.However,given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations,these ADHD-related white matter alterations are still far from informing clinical practice.Future studies with larger and more homogeneous samples are needed to validate the consistency of current results;advanced dMRI techniques can help to generatemuchmore precise estimation of whitematter pathways and assure specific fiber configurations;and finally,dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.

Structural and functional connectivity in traumatic brain injury

Traumatic brain injury survivors often experience cognitive deficits and neuropsychiatric symptoms.However,the neurobiological mechanisms underlying specific impairments are not fully understood.Advances in neuroimaging techniques（such as diffusion tensor imaging and functional MRI）have given us new insights on structural and functional connectivity patterns of the human brain in both health and disease.The connectome derived from connectivity maps reflects the entire constellation of distributed brain networks.Using these powerful neuroimaging approaches,changes at the microstructural level can be detected through regional and global properties of neuronal networks.Here we will review recent developments in the study of brain network abnormalities in traumatic brain injury,mainly focusing on structural and functional connectivity.Some connectomic studies have provided interesting insights into the neurological dysfunction that occurs following traumatic brain injury.These techniques could eventually be helpful in developing imaging biomarkers of cognitive and neurobehavioral sequelae,as well as predicting outcome and prognosis.

An abnormal resting-state functional brain network indicates progression towards Alzheimer's disease

Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer＇s disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations （nor- mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer＇s disease） using the Alzheimer＇s Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer＇s disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest- ing-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addi- tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and AIz- heimer＇s disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.

Lithium Quantum Consciousness

Conscious agency is considered to be founded upon a quantum state of mind . An original synthesis, called “Lithium Quantum Consciousness” (LQC), proposes that this quantum state utilises lithium-6 (spin-1) qutrit nuclear magnetic resonance (NMR) quantum information processing (QIP) in the connectome (brain-graph). In parallel to the connectome’s processing of physiological controls, perception, cognition and intelligence via quantum electrodynamics (QED), the connectome also functions via its dynamic algebraic topology as a unitary transceiver antenna laced with lithium-6 nuclei which are spin-entangled with each other and with the environmental vortical gluon field via quantum chromodynamics (QCD). This unitary antenna (connectome) bestows the self its unity of consciousness within an intertwined-history multi-agent environment. An equivalence is proposed between Whitehead’s occasions of experience and topological spacetime instantons in the vortical gluon field. Topological spacetime instantons pervade the vortical gluon field in a quantum information network of vortex interactions, herein termed the “instanton-net”, or “Instanet” [sic]. The fermionic isotope lithium-6 has a very low nuclear binding energy and the smallest non-zero nuclear electric quadrupole moment of any stable nucleus making it susceptible to quantum chromodynamic (QCD) interaction with the vortical gluon field and ideal for spin-1 qutrit NMR-QIP. The compact spherical atomic orbital of lithium provides ideal rotational freedom inside tetrahedral water cages in organo6Li+(H2O)4 within which the lithium nucleus rapidly tumbles for NMR motional narrowing and long decoherence times. Nuclear spin-entanglement, among water-caged lithium-6 nuclei in the connectome, is a spin-1 qutrit NMR-QIP resource for conscious agency. By contrast, similar tetrahedral xenon cages in organo6Li+Xe4 excimers are postulated to decohere the connectome’s NMR-QIP due to xenon’s NMR signal being extremely sensitive to its molecular environment. By way of this quantum neurochemistry, lithium is an effective psychiatric medication for enhancing mood and xenon is an effective anaesthetic.

Metabolomic and structural brain connectomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder

OBJECTIVE Major depressive disorder(MDD) is a highly heterogeneous mental illness.Further classification may help characterize its heterogeneity.The purpose of this study was to examine metabolomic and brain connectomic associations with traditional Chinese medicine(TCM) diagnostic classification of MDD.METHODS Fifty unmedicated depressed patients were classified into Liver Qi Stagnation(LQS,n=30) and Heart and Spleen Deficiency(HSD,n=20) subtypes according to TCM diagnosis.Healthy volunteers(n=28) were included as controls.Gas chromatography-mass spectrometry(GC-MS) and diffusion tensor imaging were used to detect serum and urinary metabolomic profiles and whole-brain white matter connectivity,respectively.RESULTS In metabolomic analysis,28 metabolites were identified for good separations between TCM subtypes and healthy controls in serum and urine samples.While both TCM subtypes had similar profiles in proteinogenic branched-chain amino acids and energy metabolism-related metabolites that were differentiated from healthy controls,the LQS subtype additionally differed from healthy controls in multiple amino acid metabolites that are involved in the biosynthesis of monoamine and amino acid neurotransmitters.Several metabolites are differentially associated with the two subtypes.In connectomic analysis,The LQS subtype showed significant differences in multiple network metrics of the angular gyrus,middle occipital gyrus,calcarine sulcus,and Heschl′ s gyrus when compared to the other two groups.The HSD subtype had markedly greater regional connectivity of the insula,parahippocampal gyrus,and posterior cingulate gyrus than the other two groups,and microstructural abnormalities of the frontal medial orbital gyrus and middle temporal pole.The insular betweenness centrality was strongly inversely correlated with the severity of depression and dichotomized the two subtypes at the optimal cutoff value with acceptable sensitivity and specificity.CONCLUSION The LQS subtype may represent an MDD subpopulation mainly characterized by abnormalities in the biosynthesis of monoamine and amino acid neurotransmitters,closer associations with stress-related pathophysiology,and aberrant connectivity of the audiovisual perception-related temporal-occipital network,whereas the HSD subtype is more closely associated with hyperconnectivity and microstructural abnormalities of the limbicparalimbic network.Certain metabolomic and connectomic variables are potential biomarkers for TCM diagnostic subtypes which is perhaps an alternative classification for depressive disorders.

Whole-brain Optical Imaging:A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level

The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons.Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain.Optical approaches can achieve submicron lateral resolution and achieve“optical sectioning”by a variety of means,which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level.Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues.Combined with various fluorescent labeling techniques,whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells,circuits,and blood vessels.In this review,we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.

Non-traditional cognitive brain network involvement in insulo-Sylvian gliomas:a case series study and clinical experience using Quicktome

Background Patients with insulo-Sylvian gliomas continue to present with severe morbidity in cognitive functions primarily due to neurosurgeons’lack of familiarity with non-traditional brain networks.We sought to identify the frequency of invasion and proximity of gliomas to portions of these networks.Methods We retrospectively analyzed data from 45 patients undergoing glioma surgery centered in the insular lobe.Tumors were categorized based on their proximity and invasiveness of non-traditional cognitive networks and traditionally eloquent structures.Diffusion tensor imaging tractography was completed by creating a personalized brain atlas using Quicktome to determine eloquent and non-eloquent networks in each patient.Additionally,we prospectively collected neuropsychological data on 7 patients to compare tumor-network involvement with change in cognition.Lastly,2 prospective patients had their surgical plan influenced by network mapping determined by Quicktome.Results Forty-four of 45 patients demonstrated tumor involvement(<1 cm proximity or invasion)with components of non-traditional brain networks involved in cognition such as the salience network(SN,60%)and the central executive network(CEN,56%).Of the seven prospective patients,all had tumors involved with the SN,CEN(5/7,71%),and language network(5/7,71%).The mean scores of MMSE and MOCA before surgery were 18.71±6.94 and 17.29±6.26,respectively.The two cases who received preoperative planning with Quicktome had a postoperative performance that was anticipated.Conclusions Non-traditional brain networks involved in cognition are encountered during surgical resection of insulo-Sylvian gliomas.Quicktome can improve the understanding of the presence of these networks and allow for more informed surgical decisions based on patient functional goals.

Neurocognitive Dysfunction After Treatment for Pediatric Brain Tumors:Subtype-Specific Findings and Proposal for Brain Network-Informed Evaluations

The increasing number of long-term survivors of pediatric brain tumors requires us to incorporate the most recent knowledge derived from cognitive neuroscience into their oncological treatment.As the lesion itself,as well as each treatment,can cause specific neural damage,the long-term neurocognitive outcomes are highly complex and challenging to assess.The number of neurocognitive studies in this population grows exponentially worldwide,motivating modern neuroscience to provide guidance in follow-up before,during and after treatment.In this review,we provide an overview of structural and functional brain connectomes and their role in the neuropsychological outcomes of specific brain tumor types.Based on this information,we propose a theoretical neuroscientific framework to apply appropriate neuropsychological and imaging follow-up for future clinical care and rehabilitation trials.

Ex vivo 100 μm isotropic diffusion MRI-based tractography of connectivity changes in the end-stage R6/2 mouse model of Huntington's disease

Background:Huntington's disease is a progressive neurodegenerative disorder.Brain atrophy,as measured by volumetric magnetic resonance imaging(MRI),is a downstream consequence of neurodegeneration,but microstructural changes within brain tissue are expected to precede this volumetric decline.The tissue microstructure can be assayed non-invasively using diffusion MRI,which also allows a tractographic analysis of brain connectivity.Methods:We here used ex vivo diffusion MRI(11.7 T)to measure microstructural changes in different brain regions of end-stage(14 weeks of age)wild type and R6/2 mice(male and female)modeling Huntington's disease.To probe the microstructure of different brain regions,reduce partial volume effects and measure connectivity between different regions,a 100μm isotropic voxel resolution was acquired.Results:Although fractional anisotropy did not reveal any difference between wild-type controls and R6/2 mice,mean,axial,and radial diffusivity were increased in female R6/2 mice and decreased in male R6/2 mice.Whole brain streamlines were only reduced in male R6/2 mice,but streamline density was increased.Region-to-region tractography indicated reductions in connectivity between the cortex,hippocampus,and thalamus with the striatum,as well as within the basal ganglia(striatum—globus pallidus—subthalamic nucleus—substantia nigra—thalamus).Conclusions:Biological sex and left/right hemisphere affected tractographic results,potentially reflecting different stages of disease progression.This proof-of-principle study indicates that diffusion MRI and tractography potentially provide novel biomarkers that connect volumetric changes across different brain regions.In a translation setting,these measurements constitute a novel tool to assess the therapeutic impact of interventions such as neuroprotective agents in transgenic models,as well as patients with Huntington's disease.

Plant multiscale networks:charting plant connectivity by multi-level analysis and imaging techniques

In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation.Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes.Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project.In plant science,network analysis has similarly been applied to study the connectivity of plant components at the molecular,subcellular,cellular,organic,and organism levels.Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype.In this review,we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities.We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants.Finally,we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.

儿童青少年时期脑功能连接梯度的发育规律

成年人脑功能网络呈现从初级皮层到联合皮层渐变的核心连接梯度模式,支持了从初级感知觉到复杂抽象认知的信息加工过程.然而,目前对于人脑网络的初级皮层-联合皮层连接梯度如何发育,及其与认知发展、基因表达的关系依然知之甚少.本研究基于两个独立的6~14岁大样本儿童青少年静息态脑功能影像数据库,定量描绘了人脑功能网络的初级皮层-联合皮层连接梯度的发育轨迹.发现该连接梯度在儿童早期已经出现,并在8~11岁从次级梯度上升为首要核心梯度;其全局指标和局部分数都具有显著的年龄效应,梯度分数变化主要位于默认网络和感知觉皮层;梯度指标与个体工作记忆能力显著正相关,且连接梯度的发育受到脑功能网络分化与整合的中介调控.通过连接组-转录组联合分析,发现核心连接梯度的发育与突触传递等基因的表达水平显著相关.研究结果对理解人脑功能连接组的发育规则,建立脑发育性障碍的临床评估具有重要价值.

Multicellular Systems Biology: Quantifying Cellular Patterning and Function in Plant Organs Using Network Science

Organ function is at least partially shaped and constrained by the organization of their constituent cells. Extensive investigation has revealed mechanisms explaining how these patterns are generated, with less being known about their functional relevance. In this paper, a methodology to discretize and quantitatively analyze cellular patterning is described. By performing global organ-scale cellular interaction mapping, the organization of cells can be extracted and analyzed using network science. This provides a means to take the developmental analysis of cellular organization in complex organisms beyond qualitative descriptions and provides data-driven approaches to inferring cellular function. The bridging of a structure- function relationship in hypocotyl epidermal cell patterning through global topological analysis provides support for this approach. The analysis of cellular topologies from patterning mutants further enables the contribution of gene activity toward the organizational properties of tissues to be linked, bridging molecular and tissue scales. This systems-based approach to investigate multicellular complexity paves the way to uncovering the principles of complex organ design and achieving predictive genotypephenotype mapping.

How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior,contributes fundamental insights into human cognition,and offers prospective treatments for brain disease.Faced with the challenges posed by imaging technologies and deep learning computational models,big data and high-performance computing(HPC)play essential roles in studying brain function,brain diseases,and large-scale brain models or connectomes.We review the driving forces behind big data and HPC methods applied to brain science,including deep learning,powerful data analysis capabilities,and computational performance solutions,each of which can be used to improve diagnostic accuracy and research output.This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible,by improving data standardization and sharing,and by providing new neuromorphic insights.

Linked brain connectivity patterns with psychopathological and cognitive phenotypes in drug-naive first-episode schizophrenia

Background Schizophrenia is considered to be a disorder of dysconnectivity characterized by abnormal functional integration between distinct brain regions.Different brain connection abnormalities were found to be correlated with various clinical manifestations,but whether a common deficit in functional connectivity(FC)in relation to both clinical symptoms and cognitive impairments could present in first-episode patients who have never received any medication remains elusive.Objective To find a core deficit in the brain connectome that is related to both psychopathological and cognitive manifestations.Methods A total of 75 patients with first-episode schizophrenia and 51 healthy control participants underwent scanning of the brain and clinical ratings of behaviors.A principal component analysis was performed on the clinical ratings of symptom and cognition.Partial correlation analyses were conducted between the main psychopathological components and resting-state FC that were found abnormal in schizophrenia patients.Results Using the principal component analysis,the first principal component(PC1)explained 37%of the total variance of seven clinical features.The ratings of GAF and BACS contributed negatively to PC1,while those of PANSS,HAMD,and HAMA contributed positively.The FCs positively correlated with PC1 mainly included connections related to the insula,precuneus gyrus,and some frontal brain regions.FCs negatively correlated with PC1 mainly included connections between the left middle cingulate cortex and superior and middle occipital regions.Conclusion In conclusion,we found a linked pattern of FC associated with both psychopathological and cognitive manifestations in drug-na¨ıve first-episode schizophrenia characterized as the dysconnection related to the frontal and visual cortex,which may represent a core deficit of brain FC in patients with schizophrenia.

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

Objective:There is increasing evidence that amyotrophic lateral sclerosis(ALS)is a progressive neurodegenerative disease impacting large-scale brain networks.However,it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression.This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.Methods:Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire difusion-weighted magnetic resonance images and construct white matter(WM)networks using tractography methods.The global and regional network properties were compared between ALS and healthy subjects.The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.Results:Compared with the healthy participants,the patients with ALS showed signifcantly decreased clustering coefcient C_(p)(P=0.0034,t=2.98),normalized clustering coefcientγ(P=0.039,t=2.08),and small‐worldnessσ(P=0.038,t=2.10)at the global network level.The patients also showed decreased regional centralities in motor and non-motor systems including the frontal,temporal and subcortical regions.Using the single-subject structural connection matrix,our classifcation model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.Conclusion:Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems,extending the classical pathophysiological understanding of ALS as a motor disorder.The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

Ultra-High Field Diffusion MRI Reveals Early Axonal Pathology in Spinal Cord of ALS mice

Background:Amyotrophic lateral sclerosis(ALS)is a disease characterized by a progressive degeneration of motor neurons leading to paralysis.Our previous MRI diffusion tensor imaging studies detected early white matter changes in the spinal cords of mice carrying the G93A-SOD1 mutation.Here,we extend those studies using ultra-high field MRI(17.6 T)and fluorescent microscopy to investigate the appearance of early structural and connectivity changes in the spinal cords of ALS mice.Methods:The spinal cords from presymptomatic and symptomatic mice(80 to 120 days of age)were scanned(ex-vivo)using diffusion-weighted MRI.The fractional anisotropy(FA),axial(AD)and radial(RD)diffusivities were calculated for axial slices from the thoracic,cervical and lumbar regions of the spinal cords.The diffusion parameters were compared with fluorescence microscopy and membrane cellular markers from the same tissue regions.Results:At early stages of the disease(day 80)in the lumbar region,we found,a 19% decrease in FA,a 9% decrease in AD and a 35% increase in RD.Similar changes were observed in cervical and thoracic spinal cord regions.Differences between control and ALS mice groups at the symptomatic stages(day 120)were larger.Quantitative fluorescence microscopy at 80 days,demonstrated a 22% reduction in axonal area and a 22% increase in axonal density.Tractography and quantitative connectome analyses measured by edge weights showed a 52%decrease in the lumbar regions of the spinal cords of this ALS mice group.A significant increase in ADC(23.3%)in the ALS mice group was related to an increase in aquaporin markers.Conclusions:These findings suggest that the combination of ultra-high field diffusion MRI with fluorescent ALS mice reporters is a useful approach to detect and characterize presymptomatic white matter micro-ultrastructural changes and axonal connectivity anomalies in ALS.