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Journal of Alzheimer’s Disease xx (20xx) x–xxDOI 10.3233/JAD-142895IOS Press

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Corpus Callosum Structure isTopographically Correlated with theEarly Course of Cognition and Depressionin Alzheimer’s Disease

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Margherita Di Paolaa,∗, Owen Phillipsa, Maria Donata Orfeia, Fabrizio Pirasa, Claudia Cacciaria,Carlo Caltagironea,b and Gianfranco Spallettaa,∗

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aDepartment of Clinical and Behavioural Neurology, IRCCS Santa Lucia Foundation, Rome, Italy7

bDepartment of Neuroscience, Tor Vergata University, Rome, Italy8

Accepted 15 January 2015

Abstract. Corpus callosum (CC) abnormalities may cause cognitive and neuropsychiatric complications due to reduced hemi-spheric integration. Over a one-year period, we investigated whether the CC structure of 20 patients with mild Alzheimer’s disease(AD) was linked to the evolution of cognitive and neuropsychiatric symptoms. We also investigated whether this anatomical-clinical relationship was localized topographically on the CC by combining voxel-based morphometry and diffusion tensorimaging approaches. We assessed patients’ global cognitive deterioration and neuropsychiatric symptoms with the Mini-MentalState Examination and the Neuropsychiatric Inventory. Increased global cognitive deterioration during the early course of ADwas significantly related to reduced white matter density (p = 0.004) and fractional anisotropy (FA) (p = 0.012) and increasedmean diffusivity (MD) (p = 0.017) at the level of the CC isthmus/splenium. Further, increased depression severity was signif-icantly related to reduced FA (p = 0.008) and increased MD (p = 0.018) at the level of the CC rostrum. These results indicatethat changes in early myelinated CC fibers, which subserve the lateral temporal and parietal cortices and are less vulnerableto damage, may be related to cognitive impairment. Furthermore, changes in late myelinated CC fibers, which connect theorbitofrontal cortices and are more vulnerable to damage, may be related to the earliest neuropsychiatric symptoms of AD, suchas depression.

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Keywords: Cognitive domain, corpus callosum, depression, diffusion tensor imaging , diffusivity, drug-free, first diagnosed AD,fractional anisotropy, mean diffusivity, mild Alzheimer’s disease, Mini-Mental State Examination, neuropsychiatric symptoms,structural MRI, volume, voxel-based morphometry

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INTRODUCTION25

Alzheimer’s disease (AD) is a neurodegenerative26

disorder with clinical manifestations of cognitive27

∗Correspondence to: Margherita Di Paola, Psy. S., PhD, IRCCSSanta Lucia Foundation, Via Ardeatina 306, 00179 Rome, Italy.Tel.: +39 06 51501121; Fax: +39 06 51501213; E-mail:m.dipaola@hsantalucia.it. and Gianfranco Spalletta, MD, PhD,IRCCS Santa Lucia Foundation, Via Ardeatina 306, 00179 Rome,Italy. Tel.: +39 06 51501575; Fax: +39 06 51501575; E-mail:g.spalletta@hsantalucia.it.

impairment that are quite linear during the course 28

of the illness [1]. By contrast, the course of the 29

neuropsychiatric manifestations, including affective, 30

psychotic, and behavioral symptoms is unpredictable. 31

Neuropsychiatric symptoms have a specific pattern 32

of temporal onset. Apathy, anxiety, and depression 33

are already present in the early stages of the illness 34

[2–5], and delusions, hallucinations, and aggression 35

are more common later in the disease course [6]. More- 36

over, with the exception of apathy, which increases 37

continuously with the progression of disease severity 38

ISSN 1387-2877/15/$35.00 © 2015 – IOS Press and the authors. All rights reserved

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2 M.D. Paola et al. / Corpus Callosum and AD Symptom Course

[2], cognitive and neuropsychiatric alterations seem39

to be independent [3], thus making AD even more40

heterogeneous.41

Many studies have investigated the brain correlates42

of cognitive deficits and neuropsychiatric symptoms43

in the early and late stages of AD pathology. They44

show that these clinical manifestations are associated45

with both regional grey matter damage ([7, 8] for a46

review) and abnormal white matter [9–12]. However,47

most studies are cross-sectional and report the brain48

changes resulting from AD in patients who are at dif-49

ferent phases of the illness.50

We previously studied white matter changes in AD51

by selectively investigating the largest commissure in52

the brain, i.e., the corpus callosum (CC), and found that53

it was already affected in the early stages of the disease54

[13–15]. Although many other studies have reported55

changes in the white matter of the CC, the precise cal-56

losal pattern modification varies from small regions to57

the entire commissure (reviewed in [15]).58

The CC is the most important structure involved59

in transmitting interhemispheric information. As the60

organization in the callosal commissure is roughly61

topographical, different patterns of interhemispheric62

disconnection may lead to different forms of callosal63

atrophy, resulting in heterogeneous functional/clinical64

disabilities. Nevertheless, the potential relationship65

between the CC structure and changes in clinical per-66

formance in AD over time is not well known. Thus, the67

present study was designed to investigate whether the68

changes in cognitive and neuropsychiatric symptoms69

in a group of mild AD patients after one year already70

correlated with the callosal structure at baseline.71

To achieve this goal, we investigated a unique patient72

population using multimodal imaging methods. We73

studied newly diagnosed mild AD patients who were74

not taking psychotropic drugs or acetylcholinesterase75

inhibitors (AChEIs). They were drug-free at base-76

line. Here, we combined macro- and microstructural77

magnetic resonance imaging (MRI) methods, such as78

voxel-based morphometry (VBM) and diffusion tensor79

imaging (DTI), to investigate whether specific cal-80

losal subregions and specific neuroimaging parameters81

were related to the clinical changes. In fact, VBM82

allows assessing macrostructural brain changes (i.e.,83

atrophy) and DTI allows measuring the microstruc-84

tural integrity of transcallosal fibers. Both have been85

shown to be impaired in AD (see [15] for a review).86

Imaging acquisition was performed only at baseline,87

whereas clinical, cognitive, and neuropsychiatric mea-88

sures were evaluated at baseline and re-evaluated89

a year later.90

MATERIALS AND METHODS 91

Subjects 92

We included patients with a diagnosis of mild AD 93

who were recruited consecutively in our memory clinic 94

in Rome, Italy. The diagnosis of probable AD was 95

made by a trained clinical neuropsychiatrist, accord- 96

ing to the revised criteria for AD published by the 97

Alzheimer’s Association Research Roundtable [16]. 98

We included only drug-free patients with a new diag- 99

nosis of AD who were not undergoing treatment with 100

AChEI and had not been treated with psychotropic 101

drugs (i.e., antidepressants, antipsychotics, anxiolyt- 102

ics, or mood stabilizers) in the previous two years. 103

Inclusion criteria were: 1) mild global cognitive 104

impairment, defined as a Mini-Mental State Exami- 105

nation (MMSE) score higher than 18 and a Clinical 106

Dementia Rating (CDR) Scale score of 1; 2) laboratory 107

values within the appropriate reference intervals; 3) 108

vision and hearing sufficient for compliance with test- 109

ing procedures (eyeglasses and/or hearing aids were 110

permissible). Exclusion criteria were: 1) major med- 111

ical illnesses (e.g., unstabilized diabetes, obstructive 112

pulmonary disease or asthma; hematologic/oncologic 113

disorders; vitamin B12 or folate deficiency, as evi- 114

denced by blood concentrations below the lower limits 115

of the reference intervals; pernicious anemia; clini- 116

cally significant and unstable active gastrointestinal, 117

renal, hepatic, endocrine or cardiovascular system dis- 118

ease; newly treated hypothyroidism); 2) comorbidity 119

of primary psychiatric or neurological disorders (e.g., 120

schizophrenia, mood depression, stroke, Parkinson’s 121

disease, seizure disorder and head injury with loss 122

of consciousness) or any other significant mental or 123

neurological disorder; 3) known or suspected his- 124

tory of drug/alcohol dependence and abuse during 125

the lifetime; 4) any potential brain abnormalities or 126

microvascular lesions appearing on conventional T2- 127

and FLAIR-scans; in particular, the presence, sever- 128

ity, and location of vascular lesions was rated by two 129

expert radiologists who used a protocol designed for 130

the Rotterdam Scan Study [17]. Generally, they were 131

considered present in cases of hyperintense lesions 132

on both proton-density and T2-weighted (see image 133

acquisition) images and rated semiquantitatively as 0 134

(none), 1 (pencil-thin lining), 2 (smooth halo), or 3 135

(large confluent) for three separate regions, i.e., adja- 136

cent to the frontal horns (frontal caps), adjacent to 137

the wall of the lateral ventricles (bands), and adja- 138

cent to the occipital horns (occipital caps). The total 139

vascular lesion load was calculated by summing the 140

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M.D. Paola et al. / Corpus Callosum and AD Symptom Course 3

Table 1Socio-demographic and clinical characteristics of 20 patients with mild Alzheimer’s disease (AD) at the first diagnostic visit and the one-year

follow-up

Subject Group Statistic

Time 0 One-year follow-up Paired t-test df p value

Gender (F/M) 11/9Age, Mean (SD) 72.75 (6.77)Range 59–85Educational level, Mean (SD) 6.9 (4.05)Range 1–18MMSE scores, Mean (SD) 23.40 (2.62) 22.50 (3.98) 1.119 19 0.277a

Range 19–28 10–28CDR value 1 (0.00) 1.15 (0.37) –2.042 19 0.55a

ADL 7.15 (1.46) 9.00 (2.71) –3.231 19 0.004a

IADL 12.4 (4.42) 15.30 (5.29) –2.964 19 0.008a

MMSE, Mini-Mental State Examination; CDR, Clinical Dementia Rating; ADL, Activities of Daily Living; IADL, Instrumental Activities ofDaily Living. a mild AD time zero < mild AD after 12 months: paired T test (when referred to a cognitive or clinical scale comparison, lowerpunctuations mean lesser impairment).

region-specific scores (range, 0–9). In the present141

study, only participants who obtained a score of 0-1142

were included; 5) lack of a “reliable” caregiver who143

was able to report to the clinic, fill in the scales, ensure144

compliance with treatment and clinical visits (MRI145

acquisition, neuropsychological and psychiatric eval-146

uation at baseline and at the 12-month follow-up), and147

contact the patient at least twice a week (one contact148

had to be a personal visit).149

After the first screening, 15 out of 40 patients were150

excluded from the initial sample because they did151

not fulfill the inclusion criteria: six had major medi-152

cal illnesses, two lacked reliable caregivers, three had153

comorbidity of primary psychiatric or neurological dis-154

orders, and four had MRI evidence of parenchymal155

abnormalities or neoplasms. Thus, 25 patients were156

included at baseline.157

After one year, the 25 AD patients underwent158

follow-up examinations to investigate the progression159

of global cognitive deterioration, performance of basic160

(ADL) and instrumental (IADL) activities of daily liv-161

ing and severity of neuropsychiatric disorders. Five162

out of the 25 patients did not complete the follow-up163

procedure: three withdrew from the study for different164

reasons (unwillingness to continue, lack of caregiver165

reliability, death) and two developed major medical166

illnesses (e.g., ictus, renal system disease). In sum-167

mary, only 20 out of the initial 25 patients carried out168

both the baseline and the follow-up procedure satis-169

factorily and were included in the study group. The170

sociodemographic, clinical, cognitive, and functional171

characteristics of the final clinical sample are summa-172

rized in Table 1.173

After the baseline diagnostic evaluation, the174

patients’ medical care was handled by a clinical175

neuropsychiatrist, who was blind to the aims of the 176

study. The clinician was able to prescribe AChEI or 177

psychotropic agents at any dosage, according to inter- 178

national and Italian Neurological Society guidelines 179

for the treatment of AD. Table 2 reports the drug treat- 180

ment of individual subjects. 181

Clinical, cognitive, and neuropsychiatric 182

assessment 183

Clinical assessment 184

To make the clinical assessment we administered 185

the MMSE [18], which provides a global index of cog- 186

nitive deterioration and is commonly used to make 187

the first dementia diagnosis and to assess its progres- 188

sion and severity. MMSE scores range from 30 (no 189

impairment) to 0 (maximum impairment). We also 190

rated the effect of cognitive impairment on functional 191

daily activities using the CDR Scale [19]. Partici- 192

pants with a CDR = 1 were classified as mild AD. 193

Finally, to assess the patients’ ability to perform the 194

activities of daily living independently, we adminis- 195

tered the ADL [20] and IADL [21]. The ADL scores 196

range from 0 (severe functional impairment) to 6 (fully 197

functioning). The IADL scale scores range from 0 198

(low functioning), dependent) to 8 (high functioning), 199

independent). 200

Cognitive assessment 201

To examine cognitive status, performance in specific 202

cognitive domains was measured using an extensive 203

neuropsychological test battery: logical reasoning was 204

assessed with Raven’s Progressive Matrices 47 [22]; 205

short-term visual memory with the Immediate Visual 206

Memory test [23]; long-term verbal and visuo-spatial 207

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Table 2Pharmacological treatment of individual AD patients after their inclusion in the study

Drug Category Acetylcholinesterase Inhibitors Antidepressants Antipsychotics Benzodiazepines

Active ingredient Donepezil Rivastigmine Galantamine Citalopram Quetiapine Alprazolam

Patients under medication (number) 7/20 9/20 3/20 8/20 1/20 3/20Dosage (mg/day equivalent values) Dosage Dosage Dosage Dosage Dosage DosagePatient 01 5Patient 02 6Patient 03 5 13Patient 04 3Patient 05 3 26 2Patient 06 5Patient 07 6 20Patient 08 6Patient 09Patient 10 16 16Patient 11 10 40Patient 12 6Patient 13 5 0.8Patient 14 6 40Patient 15 16 13 50 1Patient 16 5Patient 17 8Patient 18 3 26Patient 19 3Patient 20 10

memory with Rey’s 15-word Immediate and Delayed208

Recall and Rey’s Figure Delayed Recall [24, 25]; exec-209

utive functions with the Stroop test [26]; language210

performance with Phonological Verbal Fluency and the211

Semantic Verbal Fluency tests [27, 28]; constructional 212

praxis abilities with Rey’s Figure Copy [29]; atten- 213

tion with the Double Barrage test [30] (for details see 214

Table 3). 215

Table 3Neuropsychological scores of 20 patients with mild Alzheimer’s disease (AD) at the first diagnostic visit and at the one-year follow-up

Time 0 Mean (SD) One-year follow-up Mean (SD) Paired T test df p

General intelligence Raven’s Colored Matrices 19.05 (5.5) 17.2 (6.54) 1.603 19 0.125Short-term memory Immediate Visual Memory 16.1 (3.81) 16.5 (3.28) −0.545 19 0.592Declarative memory 15-word learning task

Immediate recall 18.75 (5.28) 19.3 (6.51) −0.516 19 0.61115 min delayed recall 1.25 (1.77) 1.20 (1.88) 0.195 19 0.847Rey’s figure15 min delayed recall 3.4 (3.77) 2.82 (4.81) 0.822 19 0.422

Executive functions Stroop test*Reading time in s 26.68 (12.2) 27.60 (10.7) −0.395 18 0.697Reading errors 0.16 (0.50) 0.15 (0.49) 0.809 18 0.429Color time in s 33 (10.92) 35.80 (15.38) −0.195 18 0.848Color errors 0.53 (1.43) 0.95 (1.47) −0.721 18 0.48Interference time in s. 73.74 (27.77) 72.60 (22.70) 0.246 18 0.809Interference errors 2.37 (3.35) 5.65 (7.36) −1.606 18 0.126

Language Phonological Verbal Fluency 18.90 (6.05) 15.75 (6.52) 2.181 19 0.042a

(cut-off 17.35)Semantic Verbal Fluency 10.40 (3.08) 10.15 (4.11) 0.27 19 0.79(cut-off 12)

Praxic abilities Copy of Rey’s figure 21.82 (8.27) 20.90 (8.98) 0.489 19 0.63Attention Double Barrage test

Time in s 124.00 (56.42) 107.80 (56.16) 1.124 19 0.275Recognitions 9.85 (3.56) 9.35 (3.26) 0.845 19 0.409Intrusions 3.65 (6.18) 2.3 (3.26) 0.884 19 0.388

amild AD time zero > mild AD after 12 months: paired T test, (when referred to a cognitive scale comparison, higher punctuations indicate lessimpairment). p = 0.003 after Bonferroni correction. *Stroop test: missing data for 1 mild AD time zero.

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Neuropsychiatric assessment216

To evaluate the severity of participants’ neu-217

ropsychiatric symptoms, we administered the Neu-218

ropsychiatric Inventory (NPI) [31]. This instrument219

measures 12 neuropsychiatric symptoms that are220

common in dementia: delusions, hallucinations, agita-221

tion/aggression, dysphoria/depression, anxiety, eupho-222

ria/elation, apathy, disinhibition, irritability/lability,223

aberrant motor behaviors, nighttime behavioral dis-224

turbances, and appetite/eating disturbances (for details225

see Table 4).226

The clinical, cognitive, and neuropsychiatric assess-227

ments were performed both at baseline and at the228

one-year follow-up. Consent was obtained from all229

participants, in agreement with the Declaration of230

Helsinki, and the Santa Lucia Foundation Research231

Ethics Committee approved the study.232

MRI data acquisition233

All MRI data were acquired with a 3T Allegra234

MRI system (Siemens, Erlangen, Germany) using235

a birdcage head coil. Scans were collected in a236

single session with the following pulse sequences:237

1) proton density and T2-weighted double turbo238

spin-echo (SE) sequences were acquired in trans-239

verse planes (time repetition [TR]: 4500 ms, time240

echo [TE]: 12 ms, time to inversion [TI]: 112 ms,241

field of view [FOV]: 230 × 3 × 172 mm, matrix:242

320 × 3 × 240, slice thickness: 5 mm, number of243

slices: 24); 2) fluid-attenuated inversion recov-244

ery was acquired in the same planes as the245

SE sequence (TR/TE/TI: 8500/109/2000 ms; FOV:246

230 × 3 × 168 mm, matrix: 256 × 3 × 256, slice thick-247

ness: 5 mm, number of slices: 24); 3) T1-weighted 3D248

images, with partitions acquired in the sagittal plane249

using a modified driven equilibrium Fourier transform 250

sequence (TE/TR/TI: 2.4/7.92/910 ms, flip angle: 15◦, 251

1 mm3 isotropic voxels); and 4) diffusion-weighted 252

images were acquired using echo-planar imaging 253

(SE-EPI, TE/TR = 89/8500 ms, 52 axial slices, band- 254

width = 1860 Hz/vx, voxel size 1.5×1.5 × 2.0 mm3) 255

with 12 isotropically distributed orientations for 256

the diffusion-sensitizing gradients at a b-value of 257

1000s·mm– 2 and 2 b = 0 images. 258

Imaging data were collected only at baseline. 259

Callosal white matter density (VBM approach) 260

Images were processed and analyzed using VBM 261

[32] in the statistical parametric mapping framework 262

(SPM8, Wellcome Department of Imaging Neuro- 263

science, University College, London, UK). To improve 264

image registration, images were first manually reori- 265

ented to approximate the orientation of the ICBM-152 266

default SPM8 template. 267

According to the procedure (see http://www. 268

fil.ion.ucl.ac.uk/spm/doc/spm8 manual.pdf), we first 269

segmented (Unified segmentation) each volume in 270

native space into grey and white matter (we did not 271

change the custom default settings, thus we did not 272

obtain the cerebrospinal fluid partition). 273

Then, the Diffeomorphic Anatomical Registration 274

Through Exponentiated Lie Algebra (DARTEL, [33]) 275

Toolbox was applied to both grey and white matter 276

partitions. DARTEL is a high dimensional warping 277

process that increases the registration between indi- 278

viduals, thus leading to improved localization and 279

increased sensitivity in analyses. 280

Taking the parameter files produced by the initial 281

segmentation step, we generated rigidly aligned grey 282

and white matter images of each subject, so that they 283

Table 4Neuropsychiatric scores of 20 patients with mild Alzheimer’s disease at the first diagnostic visit and at the one-year follow-up

Neuropsychiatric Inventory Baseline (mean, SD) Follow-up (mean, SD) Paired T test df p (one-tailed)

Delusions 0.05, 0.22 0.10, 0.45 −1.000 19 0.165Hallucinations 0.00, 0.00 0.00, 0.00 – – –Agitation 3.40, 3.90 2.50, 3.19 0.846 19 0.204Depression 3.45, 3.05 3.25, 3.86 0.215 19 0.416Anxiety 3.25, 3.73 3.40, 4.27 −0.115 19 0.455Euphoria 1.10, 2.94 0.70, 1.45 0.818 19 0.211Apathy 3.95, 3.32 4.55, 4.03 −0.763 19 0.227Disinhibition 1.70, 3.44 1.05, 3.14 1.047 19 0.154Irritability 2.40, 2.96 3.05, 3.03 −0.861 19 0.200Aberrant motor behavior 2.80, 3.82 3.15, 4.63 −0.374 19 0.356Night time behavioral disturbances 2.05, 4.06 1.50, 2.76 0.470 19 0.322Appetite/eating disturbances 1.55, 3.59 2.95, 4.07 −1.812 19 0.043Total score 27.7, 20.23 26.00, 16.95 0.397 19 0.083

p = 0.003 after Bonferroni correction.

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6 M.D. Paola et al. / Corpus Callosum and AD Symptom Course

were in as close alignment as possible with the tissue284

probability maps.285

The next step was non-linear registration. The aim286

of this step is to estimate the nonlinear deformation287

that best aligns all of the tissue class images. This is288

achieved by alternating between building a template289

and registering the tissue class images with the tem-290

plate. The procedure begins by creating a mean of all291

the images that is used as an initial template. Defor-292

mations from this template to each of the individual293

images are computed and the template is then regen-294

erated by applying the inverses of the deformations to295

the images and averaging. This procedure is repeated296

a number of times. Indeed, the end result is a series of297

templates and a series of flow field files that parame-298

terize the deformations. The first template, as already299

mentioned, is based on the average of the original300

rigidly aligned gray and white matter partitions, and301

the last is the average of the Dartel registered data.302

Then we registered the last DARTEL templates and303

the individual Dartel registered images to MNI space304

with Donald McLaren’s script, which applies an affine305

transformation without resampling the output files.306

Finally, after the DARTEL processing, white matter307

partitions (unmodulated data) were smoothed using an308

8 mm full-width Gaussian kernel at half maximum and309

were entered into subsequent statistical analyses.310

The outcome of each step of the imaging prepro-311

cessing was visually inspected to verify the accuracy312

of the result.313

Callosal fractional anisotropy and mean314

diffusivity analysis (DTI processing)315

Diffusion-weighted images were processed with316

the FMRIB Software Library (FSL 4.1 http://317

www.fmrib.ox.ac.uk/fsl/). Images were corrected for318

movement and eddy current distortion. The non-319

diffusion-weighted images were skull stripped320

with FSL’s Brain Extraction Tool (BET) (http://321

fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/bet2/index.html) and322

used to mask all diffusion-weighted images [34]. A323

diffusion tensor model was fitted at each voxel to gen-324

erate fractional anisotropy (FA) and mean diffusivity325

(MD) maps. FA measures the directionality of water326

diffusion; it is sensitive to intra-voxel directional327

coherence and is often used as a measure of tissue328

integrity. MD is a measure of the average motion of329

water diffusion, independent of tissue directionality; it330

is sensitive to the microstructural architecture of cell331

membranes [35]. The native DTI data (FA and MD332

maps) we obtained were then nonlinearly registered to333

the MNI template using SPM8. Thus, DTI and VBM 334

data were in the same space. 335

Before running the statistical analysis, FA and MD 336

maps were smoothed with a Gaussian kernel of 8 mm 337

full-width at half maximum and entered into subse- 338

quent statistical analyses. 339

The outcome of each step of the imaging prepro- 340

cessing was visually inspected to verify the accuracy 341

of the result. 342

Statistical analysis 343

Differences among the clinical, cognitive, and neu- 344

ropsychiatric variables at baseline and at follow-up 345

were assessed by running paired sample t-tests. The 346

statistical analyses were performed using SPSS 21.0. 347

A p-value of <0.003 after Bonferroni correction 348

was considered as significant (Bonferroni correction 349

p = 0.05/17 number of paired t-tests carried out). 350

Correlation analyses of the clinical, cognitive, neu- 351

ropsychiatric, and neuroimaging data were performed 352

using the statistical parametric mapping framework 353

(SPM8, Wellcome Department of Imaging Neuro- 354

science, University College, London, UK). 355

First, we calculated the progression of clinical, cog- 356

nitive, and neuropsychiatric symptoms over one year 357

of the disease using the difference (we called it Delta, 358

� values) between the two time points (baseline and 359

follow-up). We calculated the Delta value so that a 360

high one always corresponded to a worsening of symp- 361

toms over the one-year course of the illness, regardless 362

of the original meaning of the clinical, cognitive, and 363

neuropsychiatric measure. For example, in the MMSE, 364

where a higher score indicates a better global cognitive 365

condition, we applied the following formula: differ- 366

ence between MMSE score at follow-up and MMSE 367

score at baseline. For the NPI, where a higher score 368

indicates a worse symptom condition, we applied the 369

following formula: difference between NPI score at 370

baseline and NPI score at follow-up. We continued in 371

the same manner for all the other scales and tests used 372

and listed in the previous session. 373

Then, we performed correlation analyses on the 374

clinical, cognitive, and neuropsychiatric Delta values 375

calculated and white matter integrity parameters (white 376

matter intensity, FA and MD maps) using the multiple 377

regression statistical model in SPM8. In each SPM8 378

matrix we entered individual clinical, cognitive, or 379

neuropsychiatric Delta values and all individual MRI 380

parameters (white matter density, FA and MD maps). 381

To control for the effect of clinical, cognitive, and neu- 382

ropsychiatric values at baseline on the main effect, we 383

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M.D. Paola et al. / Corpus Callosum and AD Symptom Course 7

entered these as covariates. Analysis of the images384

(white matter partition, FA and MD maps) was then385

restricted to the CC, choosing the option “inclusive386

mask” in the SPM window’s box, which limits the sta-387

tistical analysis to the brain region provided as a mask.388

To do this, we used a predefined 3-dimensional vol-389

ume of interest as the mask. It was drawn by one of390

the authors (M.D.P.) on three sequential sagittal slices391

of the mean white matter image (i.e., based on the392

averaged, normalized white matter partitions of all sub-393

jects included). Since VBM and DTI images were in394

the same MNI space, the same CC mask was applied395

to all of the data. The resulting statistical maps were396

thresholded at p < 0.05 FWE-corrected for multiple397

comparisons.398

RESULTS399

Demographic and clinical characteristics400

Table 1 shows patients’ sociodemographic, clinical,401

and functional characteristics.402

Tables 3 and 4 report cognitive and NPI scores at the 403

first diagnostic visit and at the one-year follow-up. 404

Statistically significant changes were found in the 405

ADL and the IADL (see Table 1), in Phonological 406

Verbal Fluency (see Table 3), and in appetite/eating 407

disturbances (see Table 4). However, these changes did 408

not survive Bonferroni correction (p = 0.003). 409

Correlation between CC parameters and delta 410

value of clinical, cognitive, and neuropsychiatric 411

scores 412

We found significant correlations in the evolution 413

of global cognitive deterioration (measured by the 414

MMSE �-values) and white matter density, FA, and 415

MD of the CC. For all three MRI parameters, the 416

significant correlation with the MMSE �-values 417

was localized at the level of the isthmus/splenium 418

(see Fig. 1, upper panel): white matter density and 419

FA correlated positively with the MMSE �-values 420

(white matter density isthmus/splenium Z = 2.69, 421

p FWE corrected = 0.004; FA isthmus/splenium 422

Fig. 1. Relationships between corpus callosum (CC) micro- and macrostructural parameters and changes in global cognitive performance anddepression severity over one year in 20 patients with Alzheimer’s disease at the first diagnostic visit. The upper panel describes the relationshipbetween MMSE �-values (axis x) and mean callosal white matter (WM) density, fractional anisotropy (FA), and mean diffusivity (MD) value(axis y), computed after applying the CC region of interest (ROI). The lower panel shows the relationship between Depression �-values (axisx) and mean callosal FA and MD value (axis y), computed after applying the CC ROI. On the extreme right panel, the CC areas of alterations(shown in the left panels) are overlaid on the CC of the white matter template in the default SPM8 on the midsagittal slice (X = 0). The datashown in the scatterplot were extracted from the statistical significance clusters. MMSE, Mini-Mental State Examination.

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8 M.D. Paola et al. / Corpus Callosum and AD Symptom Course

Z = 2.27, p FWE corrected = 0.012); MD correlated423

negatively with the MMSE �-values (MD isth-424

mus/splenium Z = 2.12, p FWE corrected = 0.017).425

No significant findings emerged when we ran cor-426

relations in the opposite direction (i.e., negative427

correlation between white matter density, FA, and428

MMSE �-values; positive correlation between MD429

and MMSE �-values).430

We also found statistically significant correlations431

between FA and MD values and changes in depres-432

sion symptoms measured by the NPI Depression433

�-values. For both DTI parameters, the correlation434

with the NPI Depression �-values was localized at435

the level of the callosal rostrum (see Fig. 1, lower436

panel). In particular, FA correlated positively with437

the NPI Depression �-values (FA rostrum Z = 2.42,438

p FWE corrected = 0.008), and MD correlated nega-439

tively with the NPI Depression �-values (MD rostrum440

Z = 2.10, p FWE corrected = 0.018). No significant441

findings emerged when we ran correlations in the oppo-442

site direction (i.e., negative correlation between FA443

and Depression �-values; positive correlation between444

MD and Depression �-values).445

No significant correlations were found between CC446

parameters and the other clinical, cognitive, and neu-447

ropsychiatric Delta values.448

DISCUSSION449

This is a longitudinal study, using multimodal imag-450

ing methods to investigate the relationship between451

CC micro- and macrostructure and the progression452

of global cognitive deterioration and neuropsychiatric453

symptom severity in early stage AD patients drug-free454

at baseline.455

Corpus callosum and cognitive aspects456

Our first finding was that a negative evolution457

of global cognitive performance over the one-year458

AD course was related to impaired isthmus/splenium459

CC macrostructure and microstructure at the first460

diagnostic visit. This means that the greater the isth-461

mus/splenium damage at the first diagnostic visit462

(lower white matter density, lower FA, and higher MD),463

the greater the evolution of global cognitive function-464

ing (i.e., the greater the MMSE score decrease over the465

one-year period).466

The macrostructural/volumetric data are in agree-467

ment with data obtained using a cross-sectional468

methodology, which revealed a correlation between469

global cognitive impairment, as measured by the470

MMSE, and CC volume [36]. In our previous article on 471

volumetric callosal changes in AD [11], we reported 472

a significant correlation between the CC isthmus and 473

global cognitive impairment. Since callosal axons arise 474

from cerebral cortex neurons, we hypothesized that the 475

correlation between global cognitive performance and 476

the structure of the CC isthmus might be a useful in vivo 477

indicator of the progress of neocortical disintegration 478

in AD. In support of this hypothesis, Ryberg and col- 479

leagues [37] carried out a cross-sectional study in the 480

LADIS population and found that the structure of the 481

CC isthmus correlated significantly with global cogni- 482

tive impairment, as assessed by the MMSE score. More 483

recently, the same authors [38] carried out a three-year 484

prospective follow-up study in a mixed elderly popu- 485

lation and found that CC atrophy predicted cognitive 486

impairment. 487

Thus, although previous studies have already ana- 488

lyzed this issue using macrostructural/volumetric data 489

from the CC [11, 37, 38], the present study is the 490

first to describe a relationship between microstruc- 491

tural/diffusivity changes of the posterior portion of the 492

CC and global cognitive performance, as measured 493

with the MMSE. The posterior callosal subregions 494

(i.e., isthmus and splenium) subserve two-thirds of 495

the higher-order processing areas of the lateral tem- 496

poral and parietal lobes [39]. Note that most cognitive 497

domains measured by the MMSE, such as orientation, 498

language, attention, and visuo-spatial function, rely on 499

these brain regions as anatomical substrates [40]. This 500

is in accordance with our findings which indicate that 501

the degree of integrity of the isthmus/splenium may 502

be correlated with the evolution of global cognitive 503

impairment in the early stages of AD. 504

The CC is not related to the evolution of single cog- 505

nitive domains. Perhaps this is due to the nature of the 506

CC, which has a comprehensive function in the brain. 507

Thus, a global cognitive value, such as the MMSE 508

score, more than single domain scores, is the elective 509

measure to be linked to the CC. 510

Corpus callosum and neuropsychiatric symptoms 511

We also found that increased severity of depressive 512

symptoms over the first year of illness was related to 513

impaired microstructural organization (decreased FA 514

and increased MD) at the level of the CC rostrum. This 515

means that the greater the impairment of the rostrum 516

at the first diagnostic visit (lower FA, higher MD), the 517

greater the progression of depression symptom severity 518

(increased NPI depression score severity over the one- 519

year period). FA measures the directionality of water 520

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M.D. Paola et al. / Corpus Callosum and AD Symptom Course 9

diffusion. Decreased FA indicates the loss of water521

directionality likely due to damage of the structural522

organization of the tissue [41]. MD is a measure of the523

average motion of water diffusion, independent of tis-524

sue directionality. It is sensitive to the microstructural525

architecture of cell membranes. A higher MD value526

implies greater diffusion of water molecules [42].527

Thus, the DTI parameters suggest that in our group528

microstructural damage was present at the level of the529

rostrum. This CC rostrum finding is intriguing because530

the rostrum contains interconnecting fibers that orig-531

inate in the orbito-frontal cortices [43]. According532

to previous studies, dysregulated orbitofrontal cortex533

connectivity is strongly related to mood regulation534

abnormalities [44, 45]. In particular, previous cross-535

sectional studies found the same results in depressed536

patients in both the pre-dementia [46] and the mild537

stage [47] of AD. Furthermore, our data are consis-538

tent with the results of neuroimaging studies on the539

anatomical correlates of depression in other neurode-540

generative disorders. Indeed, orbitofrontal and callosal541

changes have been reported in depressed subjects with542

Parkinson’s disease [48] and also in older depressed543

patients with no neurodegenerative disorders (see [49]544

for a review). Therefore, although late-life depression545

in subjects with and without dementia may have het-546

erogeneous mechanisms, it appears to have a similar547

regional profile within the CC. This is in accor-548

dance with the hypothesis of a fronto-limbic circuit549

of depression [50]. This finding is also in line with550

the assumption that white matter pathology is the key551

component of neuropsychiatric symptoms [51].552

It is also important to note that of all the neuropsy-553

chiatric dimensions investigated here only increased554

depression severity in the early stages of AD was cor-555

related with callosal changes. Indeed no significant556

correlations were found between CC parameters and557

the other neuropsychiatric Delta values. This is in line558

with previous reports [6, 52, 53] that depression is one559

of the most severe symptoms of AD already present in560

the early stage of the illness.561

The possible mechanism562

In general, the association between macro- and563

microstructural CC changes, cognitive impairment,564

and neuropsychiatric symptoms of depression is most565

likely a logical consequence of the interhemispheric566

disconnection. Most axons in the CC arise from cere-567

bral cortex cells and connect homologous areas of568

the two hemispheres. Indeed, previous research in AD569

patients has suggested that the pattern of CC atrophy570

reflects corresponding regional cortical neuronal loss, 571

possibly resulting from the degeneration of axons of 572

pyramidal neurons in cortical layer III [54]. These cal- 573

losal structural changes may lead to altered functional 574

connectivity between cerebral cortices and, finally, to 575

cognitive and neuropsychiatric dysfunction [55]. 576

Although the results of a correlational analysis can- 577

not be explained in mechanistic terms, we consider it 578

important to introduce a hypothesis about the possi- 579

ble underlying mechanism, a hypothesis that of course 580

needs to be validated by targeted studies. 581

In this regard, it is interesting to note that the cal- 582

losal fibers implicated in our results had a different time 583

of myelination. The rostrum contains late-myelinating 584

fibers and the isthmus/splenium early-myelinating 585

fibers [56]. The late mylenating fibers are comprised 586

of smaller axons and the myelin sheaths have fewer 587

myelin lamellae [57]. Therefore, these regions tend 588

to be more vulnerable to breakdown by a variety of 589

brain insults than the early myelinated fibers. Myelin 590

breakdown is an important component of the illness 591

process in AD [58]. Specifically, the fibers that myeli- 592

nate first in development are the last to be affected 593

by AD and those that myelinate much later in nor- 594

mal development are the first to be affected by the AD 595

degenerative process [58]. This matches perfectly with 596

our results, which on one hand link depression (a neu- 597

ropsychiatric symptom that sometimes precedes the 598

earliest manifestations of cognitive impairment) with 599

the late-myelinating and more vulnerable fibers in the 600

rostrum, and on the other hand relate the cognitive 601

dimension (i.e., the core clinical manifestation of AD, 602

which very often temporally follows manifestations of 603

depression) with the early-myelinating and less vulner- 604

able fibers of the isthmus/splenium. In other words, the 605

correlation we found between clinical, neuropsychi- 606

atric symptoms, and commissural tissue proprieties can 607

be explained by the myelination mechanism of Bart- 608

zokis’s hypothesis. Furthermore, it has been shown that 609

depression is not a cognitively related symptom in AD 610

[2, 4]. This supports the association among depres- 611

sion and cognition and different CC subregions and 612

pathways. 613

Limitations 614

On a related note, the analysis of imaging data 615

obtained from patients who are undergoing drug treat- 616

ment is challenging and can potentially affect study 617

results. Unfortunately, it is difficult to control for the 618

effect of this variable because there is high inter- 619

individual variability in drug selection, drug dosage 620

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10 M.D. Paola et al. / Corpus Callosum and AD Symptom Course

and administration, and start and length of treatment.621

Further, the treatment responses and dosages are likely622

to change in the same subject during the course of623

the disorder. Thus, some medication effects in our624

study should be considered as a limitation; however,625

this is generally the case in this kind of research.626

Finally, further analyses on a larger sample are627

needed.628

Other limitation is related to the lack of a normal629

control group to assess the test retest variability in the630

VBM and DTI measures. Recently it has shown [59]631

for DTI studies that test-retest reliability may not be632

evenly distributed throughout the brain whereby highly633

anisotropic white matter regions tended to show lower634

relative within-subject variability than other white or635

grey matter regions. A further limitation is related to636

reliance on the NPI instead than on a specific depres-637

sion measure that may be more sensitive to detecting638

mood symptoms.639

CONCLUSIONS640

In conclusion, the present study suggests that spe-641

cific areas of the CC structure are related to the642

evolution of clinical (cognitive and neuropsychiatric)643

phenomenology in AD patients over the first one-year644

period. Here we highlight the importance of different645

interhemispheric callosal pathways. The late myeli-646

nated rostrum fibers, which are more vulnerable to647

damage and are connected to the orbitofrontal cortex,648

are related to the appearance of the earliest symp-649

toms of AD, such as depression. The early myelinated650

isthmus/splenium fibers, which are less vulnerable to651

damage and subserve the lateral temporal and parietal652

lobe, are related to the core cognitive AD manifesta-653

tions that often follow some of the neuropsychiatric654

symptoms.655

ACKNOWLEDGMENTS656

This work was supported by the Italian Ministry of657

Health (IMA) grants: 204/GR-2009-1606835; and RC658

10-11-12-13/A.659

Our sincere thanks to Prof. Fabio Ferlazzo for his660

precious help in running the statistical analyses, and to661

Claire Montagna for improving the use of English in662

the manuscript.663

Authors’ disclosures available online (http://j-664

alz.com/manuscript-disclosures/14-2895r1).665

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