Dr. Talozzi aims to contribute to the definition of quantitative biomarkers for neurological pathologies. She has a physics background, and a master's in applied physics. She graduated with honors from Bologna University, where she pursued her Ph.D. in Biomedical and Neuromotor Sciences. During her doctoral studies, she worked primarily with magnetic resonance imaging, majoring in tractography methods, for which she was awarded a scholarship at the Neuroanatomy and Tractography Laboratory, King's College London. Subsequently, she exploited dimensionality reduction techniques for associating white matter damage with clinical symptoms within the Bordeaux University Disconnectome ERC grant. Currently, she expanded her research horizons to genetic investigations by joining the Greicius Lab. She aims to develop novel strategies for modeling risk scores for Alzheimer's pathology using long read sequencing methodologies.
Honors & Awards
Research exchange grant King's College London, Marco Polo funding, Bologna University (2018)
Travel grant AIRMM, Italian Association for Magnetic Resonance in Medicine (AIRMM) (2019)
Travel grant OHBM conference, Guarantors of Brain UK (2019)
Educational stipend ISMRM, International Society of Magnetic Resonance in Medicine (2017/2018/2020)
Top ranked OHBM Glasgow abstract, merit travel stipend., Organization of Human Brain Mapping (OHBM) (2022)
Seal of excellence for the MSCA HORIZON-MSCA-2021-PF-01-01, European Union MSCA grant (2022)
Doctor of Philosophy, Universita Degli Studi Di Bologna (2019)
Master of Science, Universita Degli Studi Di Bologna (2016)
Bachelor of Science, Universita Degli Studi Di Bologna (2013)
BS, Bologna University, Physics (2013)
MSc, Bologna University, Applied Physics (2016)
PhD, Bologna University, Biomedical and Neuromotor Sciences (2019)
Michael Greicius, Postdoctoral Faculty Sponsor
- Testing the Disconnectome Symptom Discoverer model on out-of-sample post-stroke language outcomes. Brain : a journal of neurology 2023
Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain.
2023; 6 (1): 726
Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.
View details for DOI 10.1038/s42003-023-05107-3
View details for PubMedID 37452124
View details for PubMedCentralID PMC10349117
A 3' UTR Deletion Is a Leading Candidate Causal Variant at the TMEM106B Locus Reducing Risk for FTLD-TDP.
medRxiv : the preprint server for health sciences
Single nucleotide variants (SNVs) near TMEM106B have been associated with risk of frontotemporal lobar dementia with TDP pathology (FTLD-TDP) but the causal variant at this locus has not yet been isolated. The initial leading FTLD-TDP genome-wide association study (GWAS) hit at this locus, rs1990622, is intergenic and is in linkage disequilibrium (LD) with a TMEM106B coding SNV, rs3173615. We developed a long-read sequencing (LRS) dataset of 407 individuals in order to identify structural variants associated with neurodegenerative disorders. We identified a prevalent 322 base pair deletion on the TMEM106B 3' untranslated region (UTR) that was in perfect linkage with rs1990622 and near-perfect linkage with rs3173615 (genotype discordance in two of 274 individuals who had LRS and short-read next-generation sequencing). In Alzheimer's Disease Sequencing Project (ADSP) participants, this deletion was in greater LD with rs1990622 (R2=0.920916, D'=0.963472) than with rs3173615 (R2=0.883776, D'=0.963575). rs1990622 and rs3173615 are less closely linked (R2=0.7403, D'=0.9915) in African populations. Among African ancestry individuals in the ADSP, the deletion is in even greater LD with rs1990622 (R2=0.936841, D'=0.976782) than with rs3173615 (R2=0.764242, D'=0.974406). Querying publicly available genetic datasets with associated mRNA expression and protein levels, we confirmed that rs1990622 is consistently a protein quantitative trait locus but not an expression quantitative trait locus, consistent with a causal variant present on the TMEM106B 3'UTR. In summary, the TMEM106B 3' UTR deletion is a large genetic variant on the TMEM106B transcript that is in higher LD with the leading GWAS hit rs1990622 than rs3173615 and may mediate the protective effect of this locus in neurodegenerative disease.
View details for DOI 10.1101/2023.07.06.23292312
View details for PubMedID 37461476
View details for PubMedCentralID PMC10350161
Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke.
Brain : a journal of neurology
Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing.
View details for DOI 10.1093/brain/awad013
View details for PubMedID 36928757
Longitudinal prediction of motor dysfunction after stroke: a disconnectome study
BRAIN STRUCTURE & FUNCTION
2022; 227 (9): 3085-3098
Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.
View details for DOI 10.1007/s00429-022-02589-5
View details for Web of Science ID 000879150400001
View details for PubMedID 36334132
View details for PubMedCentralID PMC9653357
Molecular biomarkers correlate with brain grey and white matter changes in patients with mitochondrial m.3243A > G mutation
MOLECULAR GENETICS AND METABOLISM
2022; 135 (1): 72-81
The mitochondrial DNA (mtDNA) m.3243A > G mutation in the MT-TL1 gene results in a multi-systemic disease, that is commonly associated with neurodegenerative changes in the brain.Seventeen patients harboring the m3243A > G mutation were enrolled (age 43.1 ± 11.4 years, 10 M/7F). A panel of plasma biomarkers including lactate acid, alanine, L-arginine, fibroblast growth factor 21 (FGF-21), growth/differentiation factor 15 (GDF-15) and circulating cell free -mtDNA (ccf-mtDNA), as well as blood, urine and muscle mtDNA heteroplasmy were evaluated. Patients also underwent a brain standardized MR protocol that included volumetric T1-weighted images and diffusion-weighted MRI. Twenty sex- and age-matched healthy controls were included. Voxel-wise analysis was performed on T1-weighted and diffusion imaging, respectively with VBM (voxel-based morphometry) and TBSS (Tract-based Spatial Statistics). Ventricular lactate was also evaluated by 1H-MR spectroscopy.A widespread cortical gray matter (GM) loss was observed, more severe (p < 0.001) in the bilateral calcarine, insular, frontal and parietal cortex, along with infratentorial cerebellar cortex. High urine mtDNA mutation load, high levels of plasma lactate and alanine, low levels of plasma arginine, high levels of serum FGF-21 and ventricular lactate accumulation significantly (p < 0.05) correlated with the reduced brain GM density. Widespread microstructural alterations were highlighted in the white matter, significantly (p < 0.05) correlated with plasma alanine and arginine levels, with mtDNA mutation load in urine, with high level of serum GDF-15 and with high content of plasma ccf-mtDNA.Our results suggest that the synergy of two pathogenic mechanisms, mtDNA-related mitochondrial respiratory deficiency and defective nitric oxide metabolism, contributes to the brain neurodegeneration in m.3243A > G patients.
View details for DOI 10.1016/j.ymgme.2021.11.012
View details for Web of Science ID 000752566300011
View details for PubMedID 34916127
Major cerebral vessels involvement in patients with MELAS syndrome: Worth a scan? A systematic review
JOURNAL OF NEURORADIOLOGY
2021; 48 (5): 359-366
Major cerebral vessels have been proposed as a target of defective mitochondrial metabolism in patients with mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes syndrome (MELAS). Cerebral angiographic techniques are not routinely performed in MELAS patients. A systematic literature review was performed to identify studies describing major vessel caliber alterations in MELAS. Twenty-three studies reporting on 46 MELAS patients were included. Alterations in major caliber vessels were present in 59% (27/46) of patients. Dilation occurred in 37% (17/46) of patients, and in 88% (15/17) of them during a stroke-like episode (SLE). Stenosis was reported in 24% (11/46) of patients: 36% (4/11) related to an SLE and 64% (7/11) to dissections or degenerative changes. During an SLE, identification of intracranial vessels dilation or stenosis could be a selection tool for new treatment protocols. Outside SLE, identification of major cerebral vessels dissections and degenerative changes may help to prevent subsequent complications.
View details for DOI 10.1016/j.neurad.2021.02.002
View details for Web of Science ID 000704381500009
View details for PubMedID 33596430
Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS
Endoscopic endonasal surgery has gained a prominent role in the management of complex skull base tumors. It allows the resection of a large group of benign and malignant lesions through a natural anatomical extra-cranial pathway, represented by the nasal cavities, avoiding brain retraction and neurovascular manipulation. This is reflected by the patients' prompt clinical recovery and the low risk of permanent neurological sequelae, representing the main caveat of conventional skull base surgery. This surgery must be tailored to each specific case, considering its features and relationship with surrounding neural structures, mostly based on preoperative neuroimaging. Advanced MRI techniques, such as tractography, have been rarely adopted in skull base surgery due to technical issues: lengthy and complicated processes to generate reliable reconstructions for inclusion in the neuronavigation system. This paper aims to present the protocol implemented in the institution and highlights the synergistic collaboration and teamwork between neurosurgeons and the neuroimaging team (neurologists, neuroradiologists, neuropsychologists, physicists, and bioengineers) with the final goal of selecting the optimal treatment for each patient, improving the surgical results and pursuing the advancement of personalized medicine in this field.
View details for DOI 10.3791/61724
View details for Web of Science ID 000682796200023
View details for PubMedID 34279490
Brain MRS correlates with mitochondrial dysfunction biomarkers in MELAS-associated mtDNA mutations
ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY
2021; 8 (6): 1200-1211
The purpose of this study was to investigate correlations between brain proton magnetic resonance spectroscopy (1 H-MRS) findings with serum biomarkers and heteroplasmy of mitochondrial DNA (mtDNA) mutations. This study enrolled patients carrying mtDNA mutations associated with Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke-like episodes (MELAS), and MELAS-Spectrum Syndrome (MSS).Consecutive patients carrying mtDNA mutations associated with MELAS and MSS were recruited and their serum concentrations of lactate, alanine, and heteroplasmic mtDNA mutant load were evaluated. The brain protocol included single-voxel 1 H-MRS (1.5T) in the medial parieto-occipital cortex (MPOC), left cerebellar hemisphere, parieto-occipital white matter (POWM), and lateral ventricles. Relative metabolite concentrations of N-acetyl-aspartate (NAA), choline (Cho), and myo-inositol (mI) were estimated relative to creatine (Cr), using LCModel 6.3.Six patients with MELAS (age 28 ± 13 years, 3 [50%] female) and 17 with MSS (age 45 ± 11 years, 7 [41%] female) and 39 sex- and age-matched healthy controls (HC) were enrolled. These patients demonstrated a lower NAA/Cr ratio in MPOC compared to HC (p = 0.006), which inversely correlated with serum lactate (p = 0.021, rho = -0.68) and muscle mtDNA heteroplasmy (p < 0.001, rho = -0.80). Similarly, in the cerebellum patients had lower NAA/Cr (p < 0.001), Cho/Cr (p = 0.002), and NAA/mI (p = 0.001) ratios, which negatively correlated with mtDNA blood heteroplasmy (p = 0.001, rho = -0.81) and with alanine (p = 0.050, rho = -0.67). Ventricular lactate was present in 78.3% (18/23) of patients, correlating with serum lactate (p = 0.024, rho = 0.58).Correlations were found between the peripheral and biochemical markers of mitochondrial dysfunction and brain in vivo markers of neurodegeneration, supporting the use of both biomarkers as signatures of MELAS and MSS disease, to evaluate the efficacy of potential treatments.
View details for DOI 10.1002/acn3.51329
View details for Web of Science ID 000647180900001
View details for PubMedID 33951347
View details for PubMedCentralID PMC8164862
From Neurosurgical Planning to Histopathological Brain Tumor Characterization: Potentialities of Arcuate Fasciculus Along-Tract Diffusion Tensor Imaging Tractography Measures
FRONTIERS IN NEUROLOGY
2021; 12: 633209
Background: Tractography has been widely adopted to improve brain gliomas' surgical planning and guide their resection. This study aimed to evaluate state-of-the-art of arcuate fasciculus (AF) tractography for surgical planning and explore the role of along-tract analyses in vivo for characterizing tumor histopathology. Methods: High angular resolution diffusion imaging (HARDI) images were acquired for nine patients with tumors located in or near language areas (age: 41 ± 14 years, mean ± standard deviation; five males) and 32 healthy volunteers (age: 39 ± 16 years; 16 males). Phonemic fluency task fMRI was acquired preoperatively for patients. AF tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. Along-tract analyses were performed, dividing the AF into 15 segments along the length of the tract defined using the Laplacian operator. For each AF segment, diffusion tensor imaging (DTI) measures were compared with those obtained in healthy controls (HCs). The hemispheric laterality index (LI) was calculated from language task fMRI activations in the frontal, parietal, and temporal lobe parcellations. Tumors were grouped into low/high grade (LG/HG). Results: Four tumors were LG gliomas (one dysembryoplastic neuroepithelial tumor and three glioma grade II) and five HG gliomas (two grade III and three grade IV). For LG tumors, gross total removal was achieved in all but one case, for HG in two patients. Tractography identified the AF trajectory in all cases. Four along-tract DTI measures potentially discriminated LG and HG tumor patients (false discovery rate < 0.1): the number of abnormal MD and RD segments, median AD, and MD measures. Both a higher number of abnormal AF segments and a higher AD and MD measures were associated with HG tumor patients. Moreover, correlations (unadjusted p < 0.05) were found between the parietal lobe LI and the DTI measures, which discriminated between LG and HG tumor patients. In particular, a more rightward parietal lobe activation (LI < 0) correlated with a higher number of abnormal MD segments (R = -0.732) and RD segments (R = -0.724). Conclusions: AF tractography allows to detect the course of the tract, favoring the safer-as-possible tumor resection. Our preliminary study shows that along-tract DTI metrics can provide useful information for differentiating LG and HG tumors during pre-surgical tumor characterization.
View details for DOI 10.3389/fneur.2021.633209
View details for Web of Science ID 000627773000001
View details for PubMedID 33716935
View details for PubMedCentralID PMC7952864
The Combination of Metabolic Posterior Cingulate Cortical Abnormalities and Structural Asymmetries Improves the Differential Diagnosis Between Primary Progressive Aphasia and Alzheimer's Disease
JOURNAL OF ALZHEIMERS DISEASE
2021; 82 (4): 1467-1473
Differential diagnosis between primary progressive aphasia (PPA) and Alzheimer's disease (AD) could be difficult if based on clinical grounds alone. We evaluated the combination of proton MR spectroscopy of posterior cingulate cortex (PCC) and quantitative structural imaging asymmetries to differentiate PPA from AD patients. A greater left-lateralized temporo-parietal atrophy (higher accuracy for the PCC, 81.4%) and metabolic neurodegenerative changes in PCC (accuracy 76.8%) was demonstrated in PPA versus AD. The combined multiparametric approach increased the accuracy to 94%in the differential diagnosis between these two neurodegenerative diseases.
View details for DOI 10.3233/JAD-210211
View details for Web of Science ID 000687052500006
View details for PubMedID 34151798
Cell signaling pathways in autosomal-dominant leukodystrophy (ADLD): the intriguing role of the astrocytes
CELLULAR AND MOLECULAR LIFE SCIENCES
2021; 78 (6): 2781-2795
Autosomal-dominant leukodystrophy (ADLD) is a rare fatal neurodegenerative disorder with overexpression of the nuclear lamina component, Lamin B1 due to LMNB1 gene duplication or deletions upstream of the gene. The molecular mechanisms responsible for driving the onset and development of this pathology are not clear yet. Vacuolar demyelination seems to be one of the most significant histopathological observations of ADLD. Considering the role of oligodendrocytes, astrocytes, and leukemia inhibitory factor (LIF)-activated signaling pathways in the myelination processes, this work aims to analyze the specific alterations in different cell populations from patients with LMNB1 duplications and engineered cellular models overexpressing Lamin B1 protein. Our results point out, for the first time, that astrocytes may be pivotal in the evolution of the disease. Indeed, cells from ADLD patients and astrocytes overexpressing LMNB1 show severe ultrastructural nuclear alterations, not present in oligodendrocytes overexpressing LMNB1. Moreover, the accumulation of Lamin B1 in astrocytes induces a reduction in LIF and in LIF-Receptor (LIF-R) levels with a consequential decrease in LIF secretion. Therefore, in both our cellular models, Jak/Stat3 and PI3K/Akt axes, downstream of LIF/LIF-R, are downregulated. Significantly, the administration of exogenous LIF can partially reverse the toxic effects induced by Lamin B1 accumulation with differences between astrocytes and oligodendrocytes, highlighting that LMNB1 overexpression drastically affects astrocytic function reducing their fundamental support to oligodendrocytes in the myelination process. In addition, inflammation has also been investigated, showing an increased activation in ADLD patients' cells.
View details for DOI 10.1007/s00018-020-03661-1
View details for Web of Science ID 000578365700001
View details for PubMedID 33034697
View details for PubMedCentralID PMC8004488
L-Dopa Modulation of Brain Connectivity in Parkinson's Disease Patients: A Pilot EEG-fMRI Study
FRONTIERS IN NEUROSCIENCE
2019; 13: 611
Studies of functional neurosurgery and electroencephalography in Parkinson's disease have demonstrated abnormally synchronous activity between basal ganglia and motor cortex. Functional neuroimaging studies investigated brain dysfunction during motor task or resting state and primarily have shown altered patterns of activation and connectivity for motor areas. L-dopa administration relatively normalized these functional alterations. The aim of this pilot study was to examine the effects of L-dopa administration on functional connectivity in early-stage PD, as revealed by simultaneous recording of functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) data. Six patients with diagnosis of probable PD underwent EEG-fMRI acquisitions (1.5 T MR scanner and 64-channel cap) before and immediately after the intake of L-dopa. Regions of interest in the primary motor and sensorimotor regions were used for resting state fMRI analysis. From the EEG data, weighted partial directed coherence was computed in the inverse space after the removal of gradient and cardioballistic artifacts. fMRI results showed that the intake of L-dopa increased functional connectivity within the sensorimotor network, and between motor areas and both attention and default mode networks. EEG connectivity among regions of the motor network did not change significantly, while regions of the default mode network showed a strong tendency to increase their outflow toward the rest of the brain. This pilot study provided a first insight into the potentiality of simultaneous EEG-fMRI acquisitions in PD patients, showing for both techniques the analogous direction of increased connectivity after L-dopa intake, mainly involving motor, dorsal attention and default mode networks.
View details for DOI 10.3389/fnins.2019.00611
View details for Web of Science ID 000472037900001
View details for PubMedID 31258465
View details for PubMedCentralID PMC6587436
Stridor-related gray matter alterations in multiple system atrophy: A pilot study
PARKINSONISM & RELATED DISORDERS
2019; 62: 226-230
The neuroanatomical substrate of stridor associated with Multiple System Atrophy (MSA) remains unclear. We evaluated stridor-related gray matter (GM) changes in MSA.36 MSA patients underwent standardized nocturnal video-polysomnography and brain MRI. Differences in GM density between MSA patients with and without stridor and a sample of 22 matched healthy controls were evaluated with Voxel Based Morphometry protocol supplemented by a specific tool (SUIT) for analysing infratentorial structures.Stridor was confirmed in 14 patients (10 MSA-cerebellar variant; 10 M; mean ± SD age = 61.6 ± 8.9years; disease duration = 5.2 ± 2.9years) and absent in 22 (11 MSA-cerebellar variant; 18 M; age = 61.4 ± 9.9years; disease duration = 4.8 ± 3.4years). Compared to MSA without stridor, patients with stridor showed higher GM density in the cerebellum (p < 0.05, corrected for the MSA-cerebellar variant and uncorrected when considering both MSA-variants) and lower in the striatum (p < 0.05, uncorrected).This preliminary study has demonstrated for the first time in MSA stridor-related GM changes in striatal and cerebellar regions. Abnormalities in these regions were previously reported in dystonic disorders affecting laryngeal muscles, suggesting the hypothesis that stridor pathophysiology is dystonia-related. These results need however to be confirmed in a larger sample of patients.
View details for DOI 10.1016/j.parkreldis.2018.11.018
View details for Web of Science ID 000476961700036
View details for PubMedID 30509725
Predicting conversion from mild cognitive impairment to Alzheimer's disease using brain H-1-MRS and volumetric changes: A two- year retrospective follow-up study
2019; 23: 101843
This study investigated the ability of magnetic resonance spectroscopy (1H-MRS) of posterior cingulate cortex (PCC) and brain volumetry to predict the progression from mild cognitive impairment (MCI) to Alzheimer's Disease (AD) on the basis of clinical classification at 2 years follow-up. Thirty-eight MCI patients, eighteen healthy older adults and twenty-three AD patients were included in this study. All participants underwent a brain-MR protocol (1.5 T GE scanner) including high-resolution T1-weighted volumetric sequence (isotropic 1mm3). Voxel-wise differences in brain volumetry were evaluated using FreeSurfer software and all volumes were normalized by the total intracranial volume (TIV). Careful localization of 1H-MRS volume of PCC was performed and data were processed with the LCModel program. MCI patients underwent a complete neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 28 months; twenty-six MCI patients (68.4%) converted to AD and twelve remained stable. At baseline these two MCI subgroups did not differ in the global cognitive level (Mini Mental State Examination, MMSE) or in any of the other cognitive domains; the NAA/ mI ratio in the PCC was able to differentiate MCI converters from those MCI that did not develop AD (p = 0.022) with a level of accuracy (AUC area) of 0.779. A significantly reduced volume of parahippocampal gyrus (p = 0.010) and fusiform gyrus (p = 0.026) were found in the converter MCI subgroup compared to the stable MCI subgroup. The combined use of both N- acetyl-aspartate (NAA)/myo-Inositol (mI) ratio and volume of parahippocampal gyrus, increases the overall accuracy (AUC = 0.910) in predicting the conversion to AD two years before the development of clinical symptoms. Additional longitudinal studies with a broader representative sample of MCI patients and longer follow-up might be helpful to confirm these results and to elucidate the role of each parameter in predicting the possible progression to AD, and also to all the other non-AD dementia subtypes.
View details for DOI 10.1016/j.nicl.2019.101843
View details for Web of Science ID 000485804400036
View details for PubMedID 31071594
View details for PubMedCentralID PMC6506639
Along-tract analysis of the arcuate fasciculus using the Laplacian operator to evaluate different tractography methods
MAGNETIC RESONANCE IMAGING
2018; 54: 183-193
We propose a new along-tract algorithm to compare different tractography algorithms in tract curvature mapping and along-tract analysis of the arcuate fasciculus (AF). In particular, we quantified along-tract diffusion parameters and AF spatial distribution evaluating hemispheric asymmetries in a group of healthy subjects.The AF was bilaterally reconstructed in a group of 29 healthy subjects using the probabilistic ball-and-sticks model, and both deterministic and probabilistic constrained spherical deconvolution. We chose cortical ROIs as tractography targets and the developed along-tract algorithm used the Laplacian operator to parameterize the volume of the tract, allowing along-tract analysis and tract curvature mapping independent of the tractography algorithm used.The Laplacian parameterization successfully described the tract geometry underlying hemispheric asymmetries in the AF curvature. Using the probabilistic tractography methods, we found more tracts branching towards cortical terminations in the left hemisphere. This influenced the left AF curvature and its diffusion parameters, which were significantly different with respect to the right. In particular, we detected projections towards the middle temporal and inferior frontal gyri bilaterally, and towards the superior temporal and precentral gyri in the left hemisphere, with a significantly increased volume and connectivity.The approach we propose is useful to evaluate brain asymmetries, assessing the volume, the diffusion properties and the quantitative spatial localization of the AF.
View details for DOI 10.1016/j.mri.2018.08.013
View details for Web of Science ID 000447960100021
View details for PubMedID 30165094
Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines
PARKINSONISM & RELATED DISORDERS
2018; 47: 64-70
In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others.We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy.When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required.The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis.
View details for DOI 10.1016/j.parkreldis.2017.11.343
View details for Web of Science ID 000425574700012
View details for PubMedID 29208345