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Accepted Manuscript
Neural substrates of motor and cognitive dysfunctions in SCA2
patients: A network based statistics analysis
G. Olivito, M. Cercignani, M. Lupo, C. Iacobacci, S. Clausi, S.
Romano, M. Masciullo, M. Molinari, M. Bozzali, M. Leggio
PII: S2213-1582(17)30069-4
DOI: doi: 10.1016/j.nicl.2017.03.009
Reference: YNICL 978
To appear in: NeuroImage: Clinical
Received date: 12 January 2017
Revised date: 7 March 2017
Accepted date: 24 March 2017
Please cite this article as: G. Olivito, M. Cercignani, M. Lupo, C. Iacobacci, S. Clausi,
S. Romano, M. Masciullo, M. Molinari, M. Bozzali, M. Leggio , Neural substrates of
motor and cognitive dysfunctions in SCA2 patients: A network based statistics analysis.
The address for the corresponding author was captured as affiliation for all authors. Please
check if appropriate. Ynicl(2017), doi: 10.1016/j.nicl.2017.03.009
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Title Page
Neural substrates of motor and cognitive dysfunctions in SCA2 patients:
a network based statistics analysis.
G. Olivito1,2 , M. Cercignani2,3, M. Lupo1 , C. Iacobacci1,4, S. Clausi1,4, S. Romano5, M. Masciullo6,
M. Molinari7, M. Bozzali2, M. Leggio1,4
1. Ataxia Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy;
2. Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy;
3.Clinical Imaging Science Center, Brighton and Sussex Medical School, Brighton, UK;
4. Department of Psychology, Faculty of Medicine and Psychology, “Sapienza” University of Rome, Rome, Italy;
5. Department of Neurosciences, Mental Health and Sensory Organs (NESMOS), “Sapienza” University of Rome -
Sant'Andrea Hospital - Rome, Italy ;
6. SPInal REhabilitation Lab, IRCCS Fondazione Santa Lucia,Rome, Italy
7. Neurorehabilitation 1 and Spinal Center, Robotic Neurorehabilitation Lab, IRCCS Santa Lucia Foundation, Rome,
Italy.
Funding: This work was supported by the Ministry of Education, Universities and Research
(MIUR)- (Grant Number C26A1329A).
Corresponding author:
Dr. Giusy Olivito, PhD
Ataxia Laborarory, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy.
Telephone number: #39-06-51501547
Fax number: #39-06-51501213
E-mail address: g.olivito@hsantalucia.it
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ABSTRACT
Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease
characterized by a progressive cerebellar syndrome, which can be isolated or associated with
extracerebellar signs. It has been shown that patients affected by SCA2 present also cognitive
impairments and psychiatric symptoms.
The cerebellum is known to modulate cortical activity and to contribute to distinct functional
networks related to higher-level functions beyond motor control. It is therefore conceivable that one
or more networks, rather than isolated regions, may be dysfunctional in cerebellar degenerative
diseases and that an abnormal connectivity within specific cerebello-cortical regions might explain
the widespread deficits typically observed in patients.
In the present study, the network-based statistics (NBS) approach was used to assess differences in
functional connectivity between specific cerebellar and cerebral “nodes” in SCA2 patients. Altered
inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in
the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar
lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions.
The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral
regions and that the clinical symptoms may be specifically related with connectivity changes
between motor and non-motor cerebello-cortical nodes.
Keywords:
Cerebellum, cerebral cortex, resting-state fMRI, functional connectivity, nodes
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1. Introduction
Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease
involving the cerebellum. Neuropathological studies confirmed a pattern of grey matter (GM) loss
to affect the cerebellar vermis and hemispheres with sparing of the vermian lobules I,II (lingula)
and X (nodulus) and of the hemispheric lobules I,II (lingula) and Crus II (Della Nave et al., 2008a)
as well as a diffuse damage of the brainstem and cerebellar white matter (WM) (Durr et al., 1995,
Gilman et al., 1996, Estrada et al., 1999).
In addition to typical motor deficits (Takahashi et al., 2010), the presence of cognitive impairments
in subjects with degenerative ataxia has long been debated (Fehrenbach et al., 1984). Recently, the
cognitive performances of SCA 2 patients have been exhaustively investigated showing that the
patients may present with impairment in several cognitive and emotional domains (Klinke et al.,
2010; Sokolovsky et al., 2010, D’Agata et al., 2011; Fancellu et al., 2013; Moriarty et al., 2016).
The evidence of motor, cognitive, and emotional impairments in presence of cerebellar damage has
been linked to alterations of cerebro-cerebellar networks (Broich et al., 1987; Clausi et al., 2009;
Komaba et al., 2000; Baillieux et al., 2010).
Indeed, the cerebellum has extensive projections to and from cortical regions by means of middle
and superior cerebellar peduncles, the main afferent and efferent cerebellar white matter (WM)
tracts. These connections are known to be strictly controlateral and to be spatially and functionally
organized in distinct parallel loops (Middleton and Strick, 1994; Ramnani, 2006), thus contributing
to distinct functional networks (Allen et al., 2005; Habas et al., 2009; De Vico Fallani et al., 2006)
clearly related to different functional processes. Within this complex neural system the role of the
cerebellum is to integrate multisensory information and then send them back to cerebral cortex
(Leggio and Molinari, 2015). More specifically, it has been proposed that the cerebellum modulates
the cortical activity (Di Lazzaro et al., 1994; Middleton and Strick, 2000) by detecting predictable
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sequences and allowing an optimized feedforward control that is necessary to accomplish the
different functions successfully (Leggio et al., 2011).
Therefore, it is conceivable that an abnormal connectivity within specific cerebello-cortical circuits
might explain the widespread deficits typically observed in SCA2 patients and that one or more
networks, rather than isolated regions, might be dysfunctional.
Consistent with this hypothesis, a reduction of brain size has been reported in patients with SCA2,
involving not only cerebellum and brainstem, but also other cortical and subcortical areas, such as
frontal regions, primary sensorimotor cortex, temporo-mesial and parahyppocampal regions,
substantia nigra, middle striatum, and thalamus (Estrada et al.,1999; Brenneis et al., 2003; Fancellu
et al., 2013, Mercadillo et al., 2014). All these regions are known to be reciprocally connected with
the cerebellum (Schmahmann, 1991; Schmahmann and Pandya, 1997; Middleton and Strick, 2001),
indicating that several targets of cerebellar projections, including both motor and non-motor areas,
are affected in patients with SCA2.
We hypothesize that cerebellar dysfunctions affect long- distance regions of the brain and clinical
symptoms are related with changes in functional connectivity (FC) within specific cerebello-cortical
networks.
The investigation of FC may provide important information to further characterize the neural basis
and examine the integrity of cerebellar and cerebral networks in SCA2 patients. Indeed FC allows
the relationship between the neuronal activation patterns of anatomically separated brain regions to
be described (van den Heuvel and Hulshoff Pol, 2010). Amongst the available methods to
investigate the brain functional connectivity, resting-state fMRI (RS-fMRI) has been proven reliable
and easy to implement (Biswal et al., 1997) and it is particularly suitable for the study of a complex
structure like the cerebellum is, in which the function of each subregion is defined by its connection
with specific brain areas (Schmahmann and Pandya, 1997; Middleton and Strick, 2001).
While structural patterns associated with cerebellum and cerebral cortex have been largely
investigated in SCA2, the few studies, that addressed FC, have used resting-state fMRI approaches
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limited to investigate specific structures using regions chosen a priori through seed-based analysis
(Hernandez‐Castillo et al., 2015a) or meaningful functional networks through independent
component analysis (ICA) (Hernandez‐Castillo et al., 2015a; Cocozza et al., 2015). Indeed, the
analysis of pre-defined seed regions or specific networks only represents a small proportion of the
brain, thus they may not be able to provide a complete picture of how the connectome is affected in
by SCA2. Taking into account these limits, in the present study we aimed to investigate the whole-
brain functional organization associated with cerebellar structural degeneration in SCA2 by
applying the whole-brain analysis driven by graph theory, a mathematical approach that describes
complex systems as networks (Bullmore and Sporns, 2009; Rubinov and Sporn, 2010). In essence,
the brain is represented by a graph, composed by number of regions (nodes) that are functionally
connected to each other by the edges. The nodes can be defined anatomically or functionally, and
edges can be computed from RS-fMRI data. A graph can be represented mathematically by a matrix
(connectivity matrix), with each row/column identifying node, and the corresponding value
indicating the edge weight. Connectivity matrices can be compared using appropriate statistical
tools. Specifically, the Network Based Statistics (NBS) (Zalensky et al., 2010; Han et al., 2013), is
a validated statistical method to deal with the multiple comparisons problem when analyzing
connectivity matrices (or graphs). NBS can be used to identify connections and sub-networks
associated with an experimental effect or showing a between-group difference. (Zalenski et al.,
2010; Wen and Hsieh, 2016).
2. Materials and Methods
2.1 Subjects
Nine patients with SCA2 [F/M=6/3; mean age ± SD = 47.6± 10.2 years], were recruited from the
Ataxia Lab of Foundation Santa Lucia Hospital. Both in-patients (admitted for rehabilitation) and
out-patients (followed up at the clinic) were included. At the time of assessment, all patients had
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more than 6 months of illness from the genetically confirmed diagnosis. The absence of any extra-
cerebellar lesion was investigated by an expert neuroradiologist and performed by visual inspection
of the T2-weighted MRI scans acquired as part of this research study.
All patients underwent a comprehensive neurological examination. They showed a pure cerebellar
syndrome. Only CA-2 presented a Babinski sign. Cerebellar motor deficits were assessed using the
International Cooperative Ataxia Rating Scale (ICARS, Trouillas et al., 1997). ICARS global score
ranges from 0 (absence of any motor deficit) to 100 (presence of motor deficits at the highest
degree). Demographic characteristics and total motor scores of the patients are reported in Table 1.
A group of 33 healthy subjects (HS) [F/M=21/12] ranging from 40 to 60 years of age [mean age ±
SD = 50.8 ± 8.8 years] with no history of neurological or psychiatric illness were also recruited as
control group. A T-test analysis ensured that there was no significant difference in the mean age
between the two groups (p=0.34).
All patients were examined extensively through a neuropsychological protocol, covering all
cognitive domains, including : a) verbal long-term memory: 15-Word List (Immediate and Delayed
recall) (Carlesimo et al., 1996); b) verbal and visuospatial short-term memory: Digit span and the
Corsi Block Tapping task (Monaco et al., 2013); c) executive functions: Phonological Word Fluency
(Carlesimo et al., 1996) and Modified Card Sorting Test (Heaton et al., 2000); d) Reasoning:
Raven’s Coloured Progressive Matrices (Carlesimo et al., 1996); e) constructional praxis: copy of
Complex Rey’s Figure (Carlesimo et al., 2002); f) language: Naming objects subtest of the BADA
(‘‘Batteria per l’Analisi dei Deficit Afasici’’, Italian for ‘‘Battery for the analysis of aphasic
deficits’’) (Miceli et al., 1991).
The results of the neuropsychological assessment are reported in Table 2.
This research study was approved by the Ethics Committee of Santa Lucia Foundation, according to
the principles expressed in the Declaration of Helsinki. Written informed consent was obtained
from each subject.
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2.2 MRI acquisition protocol
All subjects underwent an MRI examination at 3T (Magnetom Allegra, Siemens, Erlangen,
Germany) that included the following acquisitions: 1) dual-echo turbo spin echo [TSE] (TR = 6190
ms, TE = 12/109 ms); 2) fast-FLAIR (TR = 8170 ms, 204TE = 96 ms, TI = 2100 ms); 3) 3D
Modified Driven Equilibrium Fourier Transform (MDEFT) scan (TR = 1338 ms, TE = 2.4 ms,
Matrix = 256 × 224 × 176, in-plane FOV = 250 × 250 mm2, slice thickness = 1 mm); 3) T2*
weighted echo planar imaging (EPI) sensitized to blood oxygenation level dependent imaging
(BOLD) contrast (TR: 2080 ms, TE: 30 ms, 32 axial slices parallel to AC-PC line, matrix: 64×64,
pixel size: 3×3 mm2, slice thickness: 2.5 mm, flip angle: 70°) for resting state fMRI. BOLD echo
planar images were collected during rest for a 7 min and 20 s period, resulting in a total of 220
volumes. During this acquisition, subjects were instructed to keep their eyes closed, not to think of
anything in particular, and not to fall asleep. The TSE scans of patients, acquired as part of this
research study, were reviewed by an expert neuroradiologist in order to characterize the brain
anatomy and determine the presence of macroscopic structural abnormalities involving
extracerebellar structures. For the control group, conventional MRI scans were inspected in order to
exclude any pathological conditions according to the inclusion criteria.
2.3 Resting state fMRI data preprocessing
Data were pre-processed using Statistical Parametric Mapping [Wellcome Department of Imaging
Neuroscience; SPM8 (http://www.fil.ion.ucl.ac.uk/spm/)], and in-house software implemented in
Matlab (The Mathworks Inc, Natick, Massachussetts, USA). For each subject, the first four
volumes of the fMRI series were discarded to allow for T1 equilibration effects. The pre-
processing steps included correction for head motion, compensation for slice-dependent time shifts,
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normalization to the EPI template in MNI coordinates provided with SPM8, and smoothing with a
3D Gaussian Kernel with 8mm3 full-width at half maximum. For each data set the motion
parameters estimated during correction were checked to ensure that the maximum absolute shift did
not exceed 2 mm and the maximum absolute rotation did not exceed 1.5°. The global temporal drift
was removed using a 3rd order polynomial fit and the signal was regressed against the realignment
parameters, and the signal averaged over whole brain voxels, to remove other potential sources of
bias. Then, all images were filtered by a phase-insensitive band-pass filter (pass band 0.01-0.08
Hz) to reduce the effect of low frequency drift and high frequency physiological noise. Every
participant’s MDEFT was segmented in SPM in order to estimate the total grey matter (GM)
volume. This quantity was compared (using a two-sample T-test) between patients and controls to
exclude the presence of macroscopic atrophy in patients.
3. Statistical Analysis
3.1 Network Based Statistics
In order to obtain a connectivity matrix for each participant, we first identified a set of 116 nodes
defined by the automated anatomical labelling (AAL) atlas. Each node’s mean time course was
calculated as the average of the fMRI time series from all voxels within a given region. Correlation
matrices were then obtained calculating the correlation between all pairs of nodes’ mean signals as
described by Serra and colleagues (2016). In this way, we were able to assess differences in
functional connectivity (FC) between specific cerebellar and cerebral “nodes”. The “Networks-
based statistics” (NBS) tool developed by Zalesky and co-authors (2010) was used for statistical
comparison. A two-sample t-test was used to compare FC matrices between patients and controls,
with 5000 permutations and setting the significant p-value at 0.05 corrected for multiple
comparisons by using NBS correction (Zalesky et al., 2010).
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4. Results
4.1 Neuropsychological assessment
The neuropsychological assessment revealed the presence of selective and very slight impairments
in some patients but did not show clear evidence of general cognitive impairment. Indeed, some
patients displayed values below the cut-off in word fluency test and backward digit span (CA5),
forward digit span (CA7), and Wisconsin Card Sorting Test (CA6) (see Table 2). These results are
consistent with findings that patients who are affected by cerebellar damage do not present with
marked cognitive deficits.
4.2 Resting-state fMRI data results
No significant differences were found between total GM volumes of patients [mean ± SD = 655.7
± 51.5] and controls [mean ± SD = 644.2 ± 49.1] as assessed by the T-test analysis (p=0.26).
NBS analysis showed altered inter-nodal connectivity between cerebellum and several cerebral
regions throughout the whole brain. Overall, 62 nodes and 110 edges showed differences in SCA2
brains compared to control ones while 57 edges and 35 nodes survived after Bonferroni correction
for multiple comparisons (FWE= .05) (Fig 1a). According to the cerebellar functional topography,
cerebellar nodes in the posterior cerebellum, such as Crus I and Crus II, showed reduced FC with
nodes in cortical regions implicated in cognition and emotion, such as superior (SFg) and middle
(MFg) frontal gyrus. Similarly, cerebellar nodes in the motor anterior cerebellum, such as lobule
III, IV, V, and vermis IV-V, showed reduced FC with nodes in the cortical regions related to motor
control, such as precentral (PrG) and postcentral (PcG) gyrus, Rolandic Operculum (RO),
supplementary motor area (SMA) (Fig.1b).
Finally, reduced inter-nodal FC was found between cerebellar lobule VI and both cognitive and
motor regions in the cerebral cortex, including supramarginal gyrus and supplementary motor area.
A similar pattern was found in the vermis lobule VI, showing decreased functional connectivity
with regions in the rolandic and frontal operculum as well as the supramarginal area. Detailed
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results of NBS analysis are reported in table 3 that shows the cerebello-cortical edges of significant
FC decrease in SCA2 patients.
Figure1. a) Network of significantly decreased functional connectivity in SCA2 patients as assessed by NBS analysis
(FWE= .05). The regions of the cerebello-cortical (red) and cortico-cortical (blue) modules are shown in different
colors. Bigger nodes correspond to cerebellar and cortical regions relevant to cognition and emotion; smaller nodes
correspond to cerebellar and cortical regions relevant to motor control. The brain network is visualized using the
BrainNet Viewer (https://www.nitrc.org/projects/bnv/) (Xia et al., 2013). b) Anatomical representations of cognitive
(violet) and motor (green) nodes in the cerebellum and cerebral cortex showing underconnectivity between each other.
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5. Discussion
Despite the advancing knowledge of cerebellar functions, the specific role that the cerebellum plays
in concert with other brain regions in SCA2 patients remains unclear. RS-fMRI is an ideal method
for investigating functional interactions between cerebellum and cerebral cortex in the human brain
and it may prove a useful tool for interpreting motor and non-motor impairment driven by the
cerebellar damage.
A growing body of studies explored the use of RS-fMRI in functional disconnection in
neurological and psychiatric disorders (Greicius and Menon, 2004; Greicius et al., 2007; Rombouts
et al., 2005, 2009; Liu et al., 2008; Bluhm et al., 2009; Whitfield-Gabrieli et al., 2009). Disrupted
functional cerebellar connectivity has been demonstrated in patients with schizophrenia (Liu et al.,
2008; Collin et al., 2011;), Parkinson’s Disease (Liu et al., 2013), and major depressive disorder
(Ma et al., 2013). Disruption of visual and motor connectivity has also been demonstrated in
spinocerebellar ataxia type 7 (SCA 7) supporting the theory that neurodegenerative diseases target
specific regions in large-scale networks (Seeley et al., 2007). Further, typical connectivity patterns
have been characterized in patients with autosomal dominant spinocerebellar ataxia 17 (SCA17)
(Reetz et al., 2012) suggesting that the broad range of symptoms observed in SCA17 patients may
primarily reflect the involvement of distinct functional networks affected by the cerebellar atrophy.
A disconnection syndrome has been suggested in spinocerebellar ataxia type 1 (SCA 1) by means
of intrinsic functional analysis and diffusion tensor imaging (Solodkin et al., 2011).
Accordingly, RS-fMRI studies in healthy subjects provided a detailed mapping of resting state
networks of the human cerebellum revealing that distinct networks are associated with each single
lobule (Bernard et al., 2012). van den Heuvel and Hulshoff Pol (2010) suggested that there is a
more general link between structural and functional connectivity. Indeed, it has been shown that
almost all functionally linked regions of the most often reported resting-state networks are
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structurally interconnected by known white matter tracts (van den Heuvel et al., 2009). This
suggests the existence of a general structural core of resting-state networks, supporting the notion
of an overall link between structural and functional connectivity on a whole-brain scale
(Damoiseaux and Greicius, 2009; Hagmann et al., 2008). These assumptions support the idea that
the functional heterogeneity of the cerebellum is reflected in its connectional heterogeneity and
give rise to the hypothesis that different cerebello-cortical projections and distinct functional
modules can be selectively impaired by cerebellar disorders.
In the present study the pattern of FC alterations between regions in the cerebellum and cerebral
cortex has been extensively characterized in SCA2 patients using the NBS approach, that is based
on the whole-brain analysis allowing complex systems to be described as networks (Bullmore and
Sporns, 2009; Rubinov and Sporn, 2010). We found nodes in the posterior cerebellum to show
reduced functional connectivity with nodes in cortical regions related to cognition and emotion and
nodes in the anterior cerebellum to show reduced functional connectivity with nodes in the cortical
regions related to motor control. This result is, at least in part, in line with a previous FC study in
SCA2 patients using a seed-based approach (Hernandez-Castillo et al., 2015a) and showing FC
decrease between the right posterior cerebellum and the left superior frontal gyrus, which could
impact different cognitive operations such as self-monitoring and verbal/visuospatial working
memory (Cao et al., 1998; O’Reilly et al., 2010).
Additionally, we found that in both hemispheres and vermis, lobule VI shows decreased FC with
regions related to motor control as well as to cognition and emotion (i.e. supplementary motor area,
rolandic opercula, cingulum and supramarginal gyrus). It is worth noting that the right
supramarginal gyrus is considered a region strongly implicated in emotional processing related to
social judgements and empathy (Silani et al., 2013; Hoffman et al., 2016). This finding is in line
with the evidence suggesting that SCA 2 patients are impaired in emotional behaviour (D’Agata et
al., 2011; Sokolovsky et al., 2010). The hypothesis is that cerebellar cortical neurodegeneration
associated with SCA2 may impact spatially segregated cortical regions that are functionally
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connected to the cerebellum thus affecting cerebello-cortical functional networks relevant for both
motor and non-motor functions. With respect to the latter ones, cumulative evidences have been
collected suggesting that connections between posterior cerebellum and cerebral cortex are related
to cognitive functions (Stoodley and Schmahmann 2009, 2010; Strick et al., 2009; Stoodley et al.,
2012). In our SCA2 patients, within the posterior cerebellum the prominent finding was an
impaired connectivity towards medial and superior frontal regions. These prefrontal areas have
been consistently implicated in different aspects of executive functions using both verbal and
visuospatial tasks (Reverberi et al., 2005; Crescentini et al., 2011; Langdon and Warrington, 2000).
In spite of this, in the present study only 3 patients presented impaired performances in executive
processing and verbal working memory. This datum can be explained by the fact that most
standard norms of testing do not detect cognitive impairments in cerebellar cohorts because
cerebellar patients’ symptoms are present in selective domains and very often, they can be detected
only when the patients are compared to matched healthy controls (Tedesco et al., 2011).
An important issue that needs to be discussed is that the pattern of decreased cerebello-cerebral
functional connectivity may be at least in part explained by damage of the cortical GM in SCA2.
Indeed, cerebellar atrophy has been also reported to reduce GM volume in several supratentorial
areas (Brenneis et al., 2003; Della Nave et al., 2008a). Thus, even if in the present study the total
GM volume was not significantly different between SCA2 patients and controls, the possibility of
local GM loss cannot be ruled out. An alternative explanation for the absence of significant whole
brain GM loss in patients might be that the pattern of ponto-cerebellar atrophy associated with
SCA2 pathology predominantly entails a WM damage (Della Nave et al., 2008b).
Overall, the observed pattern of inter-nodal underconnectivity is consistent with previous studies
using different RS-fMRI approaches (O’Reilly et al., 2010; Bernard et al., 2012) demonstrating in
healthy subjects the functional segregation of the cerebellum in sensorimotor and supramodal
zones, the former containing overlapping functional connectivity maps for domain-specific motor
and somatosensory cortices, the latter for prefrontal and posterior-parietal cortex, and provides
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important insight into understanding the neural circuit abnormalities in SCA2. In light of the
general link between structural and functional connectivity (van den Heuvel et al., 2009) a
comprehensive understanding of neural connectivity may require clear evidence as to whether
structural connectivity is affected in SCA2. In SCA2 patients microstructural alterations of the
cerebellar WM have been reported by Diffusion Tensor Imaging (DTI) studies, showing the
prevalent involvement of the main afferent and efferent tracts (i.e. Middle and Superior Cerebellar
Peduncle), connecting the cerebellum with both motor and non-motor cortical regions (Mandelli et
al., 2007; Della Nave et al., 2008b; Hernandez-Castillo et al., 2015b). Although in the present study
structural connectivity has not been specifically investigated, it has to be considered that
microstructural abnormalities of the cerebellar WM tracts, typically reported in SCA2 patients,
may underlie a deficient structural connectivity that impacts the cerebello-cerebral interplay and
results in a lack of functional connectivity.
Cerebellar clusters of significantly reduced functional connectivity have been recently reported by
Cocozza and colleagues (2015) only in the default mode network, executive control network and
right fronto-parietal network in patients with SCA2. However, this study used a different resting-
state approach (i.e. ICA) limited to investigate connectivity within specific functional networks
(Cocozza et al., 2015). In the present study, by using a whole-brain approach, we provide
additional evidence that extensive and segregated functional brain changes may occur as the result
of the SCA2 degenerative process.
Indeed, cerebello-cerebral functional disconnections are observed in this patient population
throughout the brain and they are consistent with the pattern of cerebellar structural alterations
mainly involving vermis and cerebellar hemispheres reported by Della Nave and colleagues (2008)
In particular, connectivity reduction involved segregated motor and cognitive cerebello-cortical
networks with the only exception of lobule VI involvement, not by chance a region in which both
motor, cognitive, and emotional functions are localized.
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It has to be underlined that we also found crus II e lobule VII to show a functional disconnection
with nodes in superior and middle frontal regions. This evidence is partially inconsistent with
previous VBM studies that have shown cerebellar grey matter reduction to spare both Crus II and
lobule VII in SCA2 patients (Brenneis et al., 2003; Ying et al., 2006; Della Nave et al., 2008).
Nevertheless, it has to be considered that a functional coherence between the two cerebellar
hemispheres has been widely demonstrated by RS-fMRI studies (Habas et al., 2009; O’Reilly et al.,
2009; Buckner et al., 2011). Thus, it is reasonable to hypothesize that the cerebellar regions that are
not directly affected by the degenerative process could suffer from the functional release of the
affected cerebellar regions and result functionally impaired.
A limitation of this study is that, due to the small number of patients, the cognitive performance has
not been directly correlated with functional connectivity alterations observed. Further
investigations are needed to support our interpretation with greater patient population.
5. Conclusion
To our knowledge this is the first study using a whole-brain approach to investigate functional
organization in SCA2 patients and to detect cerebello-cerebral inter-nodal connectivity changes
that can be associated with cerebellar structural abnormalities of SCA2.
Altogether, the present findings show that a cerebellar dysfunction may affect long-distance
regions in the cerebral cortex targeted by cerebellar projections and that specific cerebral functional
alterations derive from cerebellar structural degeneration typically associated with SCA2
pathology, thus resulting into the multifarious motor, cognitive, and emotional deficits evidenced
in patients.
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Conflict of Interest
The authors declare that they have no conflict of interest.
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Table 1. Demographic and clinical characteristics of the patients.
Case code
Age
Gender
Years of Illness
CAG repeats
ICARS TOTAL
SCORES
CA-1
42
F
1
22/39
47
CA-2
54
F
1
22/37
27
CA-3
65
M
3
22/35
27
CA-4
42
M
1
14/47
24
CA-5
42
F
1
22/39
28
CA-6
36
F
8
22/42
37
CA-7
62
F
4
22/37
31
CA-8
41
M
3
22/38
18
CA-9
44
F
13
-
28
ICARS = International Cooperative Ataxia Rating Scale
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Table 2. Patients’ Neuropsychological assessment.
Cut-off values refer to published normative data. Scores below the cut-off are marked in bold.
IR: Rey’s 15 mots short term (immediate recall); DR: Rey’s 15 mots long term (delayed recall); PM:
Progressive Matrices; WF: word fluency; fREY: Complex Rey’s Figure-copy; FDS: forward digit span;
BDS: backward digit span; FC: forward Corsi; BC: backwords Corsi; WISC: Wisconsin Card Sorting Test:
PErr (perseverative errors), TErr:total errors; NamOb(err): Naming objects (errors).
Patients
TESTs
IR
DR
PM
WF
fREY
FDS
BDS
FC
BC
WISC
NamOb
(err)
PErr TErr
CA1
45.8
10.1
28.5
33.5
33.5
5.61
4.58
3.60
-
119
108
4
CA2
46.5
10.1
29.0
19.2
26.5
5.77
2.66
4.73
4.01
108
108
5
CA3
48.7
12.5
31.0
35.2
31.5
5.60
3.50
5.57
3.77
100
100
3
CA4
46.5
11.7
33.5
24.7
34.7
5.82
4.90
6.86
5.58
100
100
4
CA5
-
-
26.0
17.3
32.2
4.47
2.37
4.43
3.58
81
81
-
CA6
45.5
8.6
30.0
23.1
30.5
5.55
3.52
3.54
3.50
138
119
2
CA7
47.3
9.7
33.0
25.5
30.0
4.13
3.19
6.15
6.01
108
92
1
CA8
42.5
13.7
35.0
19.7
32.7
4.82
2.90
5.86
4.58
119
108
1
CA9
44.8
10.1
27.5
36.5
27.5
5.61
4.58
4.60
4.58
119
119
0
Cut-off
28.53
4.69
18.96
17.35
23.76
4.26
2.65
3.46
3.17
92
92
±2
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Table 3
Table 3. Cerebello-cerebral nodes of functional underconnectivity
Pairwise brain regions
t-values*
CEREBELLUM
CEREBRAL CORTEX
Posterior
R-Crus I
L-temporal inferior
3.84
L-frontal medial
4.05
L-Crus I
R-frontal superior
4.38
R-frontal medial
5.24
L-Crus II
R-frontal superior
3.93
R-Frontal medial
4.24
R-CrusII
L-frontal superior
4.54
L-frontal medial
5.38
L-temporal inferior
3.95
R-VIIb
L-frontal medial
5.22
R-frontal medial
3.18
Intermediate
L-VI
R-supplementary motor
4.23
L-supplementary motor
4.29
R-cingulum
3.62
L-cingulum
3.64
R-supramarginal
4.51
R-VI
L-supplementary motor
3.89
R-supplementary motor
3.43
Anterior
L-III
R-frontal inferior operculum
3.35
L-frontal inferior operculum
4.93
R-III
L-frontal inferior operculum
3.61
L-rolandic operculum
3.95
R-rolandic operculum
3.43
R- IV/V
L-postcentral gyrus
3.60
VERMIS
I-II
L-frontal inferior operculum
3.96
III
L-frontal inferior operculum
4.86
R-frontal inferior operculum
3.57
L-frontal inferior,pars triangularis
4.17
R-rolandic operculum
3.83
L-postcentral gyrus
3.85
IV-V
L-precentral gyrus
4.97
R-precentral gyrus
4.79
L-frontal inferior operculum
4.32
L-rolandic operculum
3.42
R-rolandic operculum
4.00
L-supplementary motor
4.09
R-supplementary motor
3.99
L-postcentral gyrus
5.37
R-postcentral gyrus
4.64
VI
L-frontal inferior operculum
3.96
L-rolandic operculum
3.69
R-rolandic operculum
3.82
R-supramarginal
3.55
Table 3. Difference of functional connectivity into pairwise brain regions in patients with SCA2 compared to controls (p-
value <0.05 in the whole-network comparison using Network Based statistics)
*t-values are reported; R=right; L=left
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Highlights
1) A cerebellar dysfunction affects long-distance cerebral regions in SCA2 patients
2) Connectivity changes affect sensorimotor and cognitive cerebello-cortical nodes
3) Cerebellar symptoms may be related to altered cerebello-cerebral connectivity
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