Appendix e-1
Subjects
According to established criteria,e1 the diagnosis of probable behavioral variant of frontotemporal dementia (bvFTD)was based on a comprehensive evaluation including neurological history and examination, neuropsychological testing, and structural routine MRI. Clinical assessment and cognitive tests (see below) were performed by an experienced neurologist and neuropsychologist blinded to MRI results. The major clinical/behavioral features and neuropsychological findings in patients are reported in Tables e-1 and e-2. Routine MRI showed focal atrophy in the frontotemporal lobes and basal ganglia in all patients. Functional neuroimaging was obtained from 14/18 bvFTD patients, showing hypometabolism/hypoperfusion in frontal or frontotemporal regions in all cases. Eleven bvFTD patients were screened for progranulin mutations and one of them was mutated. Controls underwent a multidimensional assessment, including neurological and neuropsychological evaluation, and were included if results were normal. Exclusion criteria for control participants were any of the following: family history of dementia; significant medical illnesses or substance abuse that could interfere with cognition; major systemic, psychiatric or neurological illnesses; or focal or diffuse brain damage, including lacunae or extensive cerebrovascular disease on routine MRI analysis.
Neuropsychological assessment
Patients with bvFTD underwent a comprehensive neuropsychological assessment. Cognitive tests were performed by an experienced neuropsychologist blinded to the MRI results. The following were evaluated: global cognitive function with the Mini-Mental State Examination;e2 long term verbal memory with the memory prose,e3 delayed recall of Rey’s 15 words,e4 or semantic-related word list recall;e5 long term spatial memory with Rey’s figure delayed recall test;e6 short term verbal and spatial memory with the digit and the spatial span, respectively;e7 reasoning with Raven’s colored progressive matrices;e8 attention with attentive matrices;e9 executive functions with phonemic and semantic fluency;e3 verbal comprehension with the token test;e10 and visuo-spatial abilities with Rey’s Figure Copy Test.e6 Scores on neuropsychological tests were age-, sex-, and education-corrected by using related normative values.
MRI acquisition
Using a 3.0 Tesla Philips Intera scanner (Philips Medical Systems, Best, Netherlands), the following brain sequences were acquired for all study participants: T2-weighted spin echo (repetition time [TR] = 3500 ms, echo time [TE] = 85 ms, echo train length = 15, flip angle = 90°, 22 contiguous 5 mm-thick axial slices with a matrix size = 512 × 512, field of view [FOV] = 230mm × 184 mm); 3D T1-weighted fast field echo (TR=25 ms, TE=4.6 ms, flip angle=30, FOV=230mm × 230mm, matrix=256 × 256, slice thickness=1 mm, 220 contiguous axial slices, in-plane resolution=0.89mm × 0.89 mm); and T2*-weighted single-shot echo planar imaging (EPI) sequence for RS fMRI (TR=3000ms, TE= 35 ms, flip angle=90°, FOV=240mm × mm; matrix=128 × 128, slice thickness=4 mm, 200 sets of 30 contiguous axial slices, parallel to the AC-PC line). During scanning, subjects were instructed to remain motionless, to keep their eyes closed, and not to think anything in particular.
MRI analysis
RS fMRI data pre-processing.Using Statistical Parametric Mapping Software, Version 8 (SPM8), RS fMRI images were realigned to the first image of each session with a six degree rigid-body transformation to correct for minor head movements. None of the study participants were excluded from analysis because of motion, since the maximum cumulative translation was <1.5 mm and the maximum rotation was <0.3 degrees for all participants. Data were normalized to the SPM8 default echo-planar imaging (EPI) template using a standard affine transformation, and band-pass filtered between 0.01 and 0.08 Hz using REST software ( in order to partially remove low-frequency drifts and physiological high-frequency noise. Using REST, non-neuronal sources of synchrony between RS fMRI time series were removed by regressing out the six motion parameters estimated by SPM8, and the average signals of the ventricular cerebrospinal fluid (CSF) and white matter (WM). In order to avoid spurious correlations between neighboring voxels, as previously suggested,e11 no spatial smoothing was applied.
Construction of functional brain networks. To construct functional brain networks, we first employed an automated anatomical labeling (AAL) atlase12 to parcel the brain into 90 cortical regions of interest (ROI). Time series were extracted from each ROI by averaging the signal from all voxels within each region. Bivariate correlations between each ROI pair were obtained by calculating the Pearson’s correlation coefficient between ROI time courses. These correlation coefficients represent functional connectivity strengths between brain regions. Correlation matrices obtained from all study subjects were thresholded into binary connectivity matrices at different correlation thresholds (, resulting in unweighted graphs with the nodes representing brain regions and edges/links representing functional relationships between brain regions.e13 The number of connections surviving at a given correlation threshold can vary between subjects: in other words, even when fixing graphs from different subjects might have a different number of significant links. Therefore, previous studies did not use a fixed for all study subjects, but instead defined the so-called network sparsity, essentially meaning that they forced the total number of existing connections to be the same for all study subjects.e14-e17 Such an approach may lead to inaccurate results, since fixing a pre-defined sparsity may result in inaccurate modification of network topology:18 For instance, in networks with low average connectivity, a consistent number of insignificant correlation values might be converted into significant connections in order to achieve the imposed network degree; in contrast, in networks with high average connectivity, a large number of significant correlations might be ignored.e18 Therefore, as performed in previous studies,e19-e21 we chose to construct our graphs by fixing the same for all study subjects. Because there is not a definitive method for choosing we examined several possible network configurations for a range of values ranging from 0 to 0.9, and explored the consistency of results over this range.e11 Then, we explored network characteristics only over the range of thresholds that yielded fully connected graphs (0≤≤0.20, with increments of 0.01).
GM volumes.On 3D T1-weighted fast field echo (FFE) images, normalized mean grey matter (GM) volumes were calculated using the Structural Imaging Evaluation of Normalized Atrophy (SIENAx) software.e22 Regional GM volumes of the 90 AAL cortical and subcortical regions were measured using voxel-based morphometry and the Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL) registration methode23 in SPM8. Briefly, after segmentation of 3D FFE images into GM, WM and CSF, GM maps were normalized to the GM population-specific template generated from the complete image set using DARTEL.e23 Spatially normalized images were then modulated by the Jacobian determinants derived from the spatial normalization to ensure preservation of the overall amount of GM tissue, and smoothed with an 8-mm full-width at half maximum kernel. To mask individual images, the AAL template was then resampled in the DARTEL space, and regional GM volumes of each study subject were calculated as the mean value of all the voxels within the given AAL region.
e-References
e1.Rascovsky K, Hodges JR, Knopman D, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011;134:2456-2477.
e2.Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189-198.
e3.Novelli G, Papagno C, Capitani E, Laiacona N, Vallar G, Cappa SF. Tre test clinici di ricerca e produzione lessicale. Taratura su soggetti normali. Arch Psicol Neurol Psichiatr 1986;47:477-506.
e4.Carlesimo GA, Caltagirone C, Gainotti G. The Mental Deterioration Battery: normative data, diagnostic reliability and qualitative analyses of cognitive impairment. The Group for the Standardization of the Mental Deterioration Battery. Eur Neurol 1996;36:378-384.
e5.Mauri M, Carlesimo GA, Graceffa AMS, et al. Standardizzazione di due nuovi test di memoria: apprendimento di liste di parole correlate e non correlate semanticamente. Arch Psicol Neurol Psichiatr 1997;58:621-645.
e6.Caffarra P, Vezzadini G, Dieci F, Zonato F, Venneri A. Rey-Osterrieth complex figure: normative values in an Italian population sample. Neurol Sci 2002;22:443-447.
e7.Orsini A, Grossi D, Capitani E, Laiacona M, Papagno C, Vallar G. Verbal and spatial immediate memory span: normative data from 1355 adults and 1112 children. Ital J Neurol Sci 1987;8:539-548.
e8.Basso A, Capitani E, Laiacona M. Raven's coloured progressive matrices: normative values on 305 adult normal controls. Funct Neurol 1987;2:189-194.
e9.Spinnler H, Tognoni G. Standardizzazione e taratura italiana di test neuropsicologici. Ital J Neurol Sci 1987;6:1-120.
e10.De Renzi E, Vignolo LA. The token test: A sensitive test to detect receptive disturbances in aphasics. Brain 1962;85:665-678.
e11.Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, et al. Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 2010;5:e13788.
e12.Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273-289.
e13.Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010;52:1059-1069.
e14.Tian L, Wang J, Yan C, He Y. Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study. Neuroimage 2011;54:191-202.
e15.de Haan W, Pijnenburg YA, Strijers RL, et al. Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci 2009;10:101.
e16.He Y, Chen Z, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci 2008;28:4756-4766.
e17.Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput Biol 2010;6:e1001006.
e18.van Wijk BC, Stam CJ, Daffertshofer A. Comparing brain networks of different size and connectivity density using graph theory. PLoS One 2010;5:e13701.
e19.Meunier D, Achard S, Morcom A, Bullmore E. Age-related changes in modular organization of human brain functional networks. Neuroimage 2009;44:715-723.
e20.Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 2005;15:1332-1342.
e21.van den Heuvel MP, Stam CJ, Kahn RS, Hulshoff Pol HE. Efficiency of functional brain networks and intellectual performance. J Neurosci 2009;29:7619-7624.
e22.Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479-489.
e23.Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007;38:95-113.