• No results found

4. Experiment 2 Connectivity analysis in SCD and MCI

4.7. Experiment 2 appendix

Abbreviation Full name

Amyg Amygdala

Ang Angular Gyrus

Calc Calcarine cortex

CG Cingulate Gyrus

Cu Cuneal Cortex

FMC Frontal Medial Cortex

FOC Frontal Orbital Cortex

FP Frontal Pole

Hip Hippocampus

IOC Inferior Lateral Occipital Cortex

ITG Inferior Temporal Gyrus

ITG Inferior Frontal Gyrus

Lin Lingual Gyrus

M Motor cortex

MFG Middle Frontal Gyrus

MTG Middle Temporal Gyrus

OP Occipital Pole

ParaC Paracingulate Gyrus

ParaHip Parahippocampal Gyrus

PCu Precuneous

PosCG Postcentral Gyrus

PreCG Precentral Gyrus

SFG Superior Frontal Gyrus

SMG Supramarginal Gyrus

SOC Superior Lateral Occipital Cortex

SPL Superior Parietal Lobule

STG Superior Temporal Gyrus

TP Temporal Pole

Table 4-2. List of ROIs of the anatomical atlas. Correspondence between abbreviations showed in Figure 4-1, Figure 4-2 and Figure 4-3 and regions depicted in the Harvard-Oxford anatomical atlas (Desikan et al., 2006). Preceding letter l or r stands for left or right hemisphere, respectively. Abbreviations ending in -a, -p, -ap or -to stand for anterior part, posterior part, antero-posterior part or temporo-occipital part, respectively.

Table 4-3. Results of the correlation analysis between connectivity values and neuropsychological scores. Correlation values of each significant correlation between the significantly different links and the anatomical values and neuropsychological scores. First 14 rows depict hypo-synchronization links. Last 3 rows depict hyper-synchronization links.

The signification values were FDR corrected (Q=0.05) and the significance threshold was subsequently placed at p = 0.0094. n.s. indicates non-significant correlations. MMSE stands for Mini Mental State Examination. BNT stands for Boston Naming Test.

Hippocampal

4.8. Summary of conclusions

1. The results showed a marked disruption in brain connectivity in the SCD group. These alterations included both hypo-synchronization and hyper-synchronization between different brain regions.

2. We were able to replicate previous findings in AD literature reporting FC alterations in MCI patients. We also observed areas showing increased FC while the communication between other brain regions was dampened as compared to HC elders.

3. The observed pattern of disruption in SCD clearly paralleled that obtained for MCI patients. When we compared each of those groups to HC we identified two clearly isolated subnetworks showing different alterations. On the one hand a large posterior network emerged in both comparisons characterized by a significantly reduced FC in our suspected prodromal AD groups.

This network included several occipital, parietal and temporal regions showing decreased FC in both SCD and MCI compared to HC. Further, an anterior network comprising anterior cingulate and inferior temporal regions among others showed increased FC in both SCD and MCI compared to HC. However, what is most important about these results is the fact that the alterations found in SCD were remarkably similar to those observed in MCI, which clearly highlights that SCD should share some pathological characteristics with MCI, and thus AD.

4. SCD stage could represent the initial stages of the proposed cascading network failure along AD continuum. Previous literature supports that cognitive concerns could potentially represent a marker for AD but whether SCD already showed signs of network failure remained ignored. Our results support that the hypersynchronization known to occur in the early AD stages is already present in the SCD stage and does not seem to increase towards MCI. Furthermore, the initial decrease in FC values over posterior regions existent in SCD is further diminished in MCI patients, which is again consistent with the disconnection hypothesis of AD.

5. DMN and DAN are both affected in the preclinical stages of the disease, namely in SCD elders. Furthermore, even though MCI patients showed significant decreases in the connectivity in these two networks, their network integrity was not further compromised compared to SCD. This again points towards an early appearance of FC disruption throughout the course of the disease.

6. Posterior DMN connectivity is impaired in SCD and MCI groups.

We did not observe alterations in the aDMN. This highlights that, first, DMN components are selectively affected in AD, and second, that anterior FC disruptions might not be limited to DMN regions as predominantly studied in fMRI literature.

7. There is a clear and significant relationship between brain pathological FC patterns and cognitive deterioration in AD. More concretely, it is worth highlighting that anterior hyper-synchronization was selectively related to language impairment.

Furthermore, posterior desynchronization is associated with an overall worse cognitive state, memory performance and language.

8. Lower hippocampal volume was associated with bigger decreases in FC over posterior regions. This result highlights the pathological essence of these FC alterations. Noteworthy, this associations are highly consistent with the fact that posterior desynchronization, as opposed to anterior hyper-connection, progresses from SCD to MCI, temporarily overlapping with the gray matter impairment onset.

4.9. References

Alzheimer’s disease as revealed by resting state EEG rh ythms. International Journal of Psychophysiology : Official Journal of the International Organization of Psy c h o ph y si ol o gy . h tt p :/ / d oi . org/ 1 0 . 1 0 16 / j .i j p syc h o. 2 0 1 5 .0 2 .0 0 8 functional connections with Alzheimer’s disease progression. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience , 32(26), 8890–

9 . h t tp :/ / d oi . org/ 1 0 . 1 5 23 / JNEUR OSC I. 5 6 9 8 -1 1. 2 01 2

B rook m e ye r, R . , Joh n son , E. , Zi eg l er - Gra h a m, K. , & Ar ri gh i , H. M . (2 0 0 7 ).

Forecasting the global burden of Alzh eimer’s disease. Alzheimer’s & Dementia : The

Journal of the Alzheimer’s Association , 3 (3 ), 1 8 6 – 91 .

h t t p :/ / d oi . org/ 1 0 . 1 01 6 /j . j a lz. 2 0 0 7 . 04 . 38 1

B u c k le y, R . , Sa li n g, M . M . , Am es, D. , R o we, C . C . , La u t en sc h l a ge r, N. T. , M a c a u la y, S. L., … Ellis, K. A. (2013). Factors affecting subjective memory complaints in the A IB L a gi n g st u d y: b i oma r k ers, m em or y, a f f ec t , a n d a ge. In t e rn a t i on a l Psy c h o ge ri a t ri c s / IPA , 2 5 (8 ), 1 3 07 – 15 . h tt p :/ / d oi . org/ 1 0 . 1 017 / S1 04 1 61 02 1 30 00 66 5

Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., … Joh n son , K. A. (2 0 0 9 ). C ort i c al h u b s revea l ed b y i n t ri n si c fun c t i on a l c on n ec t i vi t y:

mapping, assessment of stability, and relation to Alzheimer’s dise ase. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience , 29(6), 1860–

7 3 . ht t p :/ / d oi . org/ 1 0 . 1 52 3 / JNEUR OSC I. 5 0 6 2 -0 8 .2 0 09 connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiology of Ag i n g, 33 (4 ), 82 8 . e1 9 -82 8 . e3 0 . h t tp :/ / d oi . org/ 1 0 . 1 0 16 / j .n eu ro b i ola gi n g. 2 0 1 1 . 06 . 024 Alzheimer’s disease: revising the NINCDS -ADRDA criteria. Lancet Neurology, 6 (8 ), 7 3 4– 46 . h tt p :/ / d oi . org/ 1 0 . 1 0 16 / S1 47 4 -44 2 2 (07 )7 01 7 8 -3 network activity distinguishes Alzheimer’s disease from health y aging: evidence fro m fu n c t i on a l M R I. Pro c e e d i n g s o f t h e Na t io n al Ac a d e my of S c i e nc e s o f t h e Un i ted S t a t e s o f Ame ri c a , 1 01 (1 3 ), 463 7 –4 2 . h t tp :/ / d oi . org/ 1 0 . 1 0 7 3/p n a s. 03 08 6 27 10 1

Groth e, M. J., Teip el, S. J., & Alzheimer’s Disease Neuroimaging Initiative. (2016). Alzheimer’s disease compared to mild cognitive impairment: an electroph ysiological st u d y. Pl o S On e , 8 (7 ), e6 8 79 2 . h t t p :/ / d oi . org/ 1 0 . 13 7 1/ j ou rn a l. p on e. 0 0 6 87 9 2 Imt i a z, B . , Tolp p a n en , A. -M . , Ki vi p e lt o, M . , & Soi n i n e n , H. (20 14 ). Fu t u re directions in Alzheimer’s disease from risk factors to prevention. Biochemical Ph a rma c o lo g y, 8 8 (4 ), 6 6 1 – 67 0. h t t p :/ /d oi . org/ 1 0 . 1 0 1 6/ j . bc p . 20 1 4 .0 1 .0 0 3

Je li c , V. , J oh a n sson , S. -E. , Al mk vi st , O. , Sh i g et a , M . , Ju li n , P. , No rd b er g, A. , … Wa h lu n d , L. - O. (2 0 0 0 ). Qu an t i t a ti ve e l ec t r o en c ep h a lo gra p h y i n mi ld c ogn i t i v e impairment: longitudinal changes and possible prediction of Alzheimer’s disease.

N e u ro bi o lo g y o f Agi n g , 2 1 (4 ), 5 33 –5 4 0 . h t tp :/ / d o i . org/ 1 0 . 1 0 16 / S0 19 7 -4 5 80 (0 0 )0 01 53 -6

Jess en , F. , Ama ri g li o, R . E. , v a n B oxt e l, M . , B ret e le r, M . , C ec c a ld i , M . , C h ét ela t , G., … Wagner, M. (2014). A conceptual framework for research on subjective cognitive d ecline in preclinical Alzh eimer’s disease. Alzheimer’s & Dementia , 1 0 (6 ), 8 44 –8 5 2 . h t tp :/ / d oi . org/ 1 0 . 10 16 / j . j a lz. 2 0 14 . 01 . 0 01 F., … Jack, C. R. (2015). Cascading network failure across the Alzheimer’s disease sp ec t ru m. Bra i n , 13 9 (2 ), 54 7 –5 6 2 . h t tp :/ / d oi . org/ 1 0 . 1 0 93 / b rai n / a wv3 3 8

Jon es, D. T. , M a c Hu ld a , M . M . , Ve mu ri , P. , M c Da d e, E. M . , Zen g, G. , S en j em, M . L., … Jack, C. R. (2011). Age-related changes in the default mode network are more a d va n c ed i n Al zh ei m er d i sea s e. N e u ro l o gy , 7 7 (1 6 ), 1 52 4 – 1 53 1.

h t t p :/ / d oi . org/ 1 0 . 1 21 2 / WN L. 0 b 0 1 3 e3 18 2 33 b3 3d

Ki m, H. J. , C h a , J. , Le e, J. -M . , Sh i n , J. S. , Jun g, N. -Y. , Ki m , Y. J. , … Seo, S. W.

(2016). Distinctive Resting State Network Disruptions Among Alzh eimer’s Disease, Su b c ort i c a l Va sc u la r D em en t i a , a nd M i xed Dem en t i a Pa t i en t s. J o u rna l of Alzheimer’s Disease , 50(3), 709–718. http://doi.org/10.3233/JAD -150637

Kli m esc h , W. (1 9 9 9 ). EEG a lp h a a n d th et a osc i l la t i on s r e fl ec t c ogn i t i ve a n d m emo r y p erf orma n c e: a r evi e w a n d a n a lysi s. Bra i n Re sea rc h . Bra i n Rese a rc h Rev i e w s , 2 9 (2–

3 ), 1 6 9– 95 . R et ri ev ed fr om h t t p :/ / www. n c b i . n lm. n i h . gov/ p u bmed / 1 0 2 09 23 1

La c h a u x, J. P. , R od ri gu e z, E. , M a rt i n eri e, J. , & Va r e la , F. J. (1 9 9 9 ). M ea su ri n g Neuroscience : The Official Journal of the Society for Neuroscience , 34(44), 14551–

9 . h t tp :/ / d oi . org/ 1 0 . 1 5 23 / JNEUR OSC I. 0 9 6 4 -1 4. 2 01 4

M a c hu ld a , M . M . , Jon es, D. T. , Vemu ri , P. , M c Da d e, E. , Avu la , R . , Prz yb e lsk i , S. ,

… Jack, C. R. (2011). Effec t of APOE ε4 status on intrinsic network connectivity in c ogn i t i ve l y n orma l e ld er l y su b j ec t s. Arc h i ve s o f N e u rol o g y , 6 8 (9 ), 1 13 1 –6 . Rabinovici, G. D., … Jagust, W. J. (2011). Relationships between beta -amyloid and fu n c t i on a l c on n ec t i vi t y i n d i ffe r en t c omp on en t s of t h e d ef a u lt mod e n et work i n dorsal and ventral attention systems for Alzheimer’s disease, amnestic mild c ogn i t i ve i mp a i rm en t . Bra i n Ima g i ng an d Beh a vi or , 9 (4 ), 7 9 0 – 80 0.

h t t p :/ / d oi . org/ 1 0 . 1 00 7 / s1 16 8 2 -0 1 4 -93 3 6 -6

R ei sb er g, B . , Sh u l ma n , M . B . , Tor ossi a n , C . , Len g, L. , & Zh u , W. (2 0 1 0 ). Ou t c ome H. J., … Klunk, W. E. (2015). Subjective cognitive complaints, pe rsonality and brain a m yl oi d -b et a i n c ogn i t i vel y n orma l o ld er a d u lt s. Ame ri c a n J o u rn a l o f Ge ri a t ric individuals at risk for Alzh eimer’s disease. Proceedings of the National Academy of S c i e nc e s o f t h e Un it e d S t a t e s o f Ame ri c a , 1 0 4 (4 7 ), 18 7 60 –5 .

normal ageing and Alzheimer’s disease using resting state fMRI with a combined

Villemagn e, V. L., & Chételat, G. (2016). Neuroimaging biomarkers in Alzheimer’s d i sea se a n d ot h e r d emen t i a s. Ag e i n g Re se a rc h Re v ie w s .

Watanabe, T., Hirose, S., Wada, H., Imai, Y., Machida, T., Shirouzu, I., … Masuda, N. (2 0 1 3 ). A p a i r wi s e ma xi m u m en t rop y m od e l a c c u ra t e l y d esc ri b es r est i n g -st a t e Alzheimer’s Disease and Amn estic Mild Cognitive Impairmen t. Brain Connectivity, 4 (8 ), 5 6 7– 57 4 . h t t p :/ /d oi . org/ 1 0 . 1 08 9 /b ra i n .2 0 14 . 02 34

Wi ep e rt , D. , Lo we, V. J. , Kn o p ma n , D. , B oev e, B . , Gra f f -R a d ford , J. , P et e rson , R . ,

… Jones, D. (2017). A rob ust biomarker of large -scale network failure in Alzheimer’s disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease M o n i to ri ng , (Ja n u a ry), 1 – 1 0 . ht t p :/ / d oi . org/ 1 0 . 1 0 16 / j .d a d m.20 1 7 .0 1 .0 0 4