アルツハイマー病と軽度認知障害の監督機能接続のグレンジャー因果関係分析の応用

要約

休息状態機能的磁気共鳴イメージング ・ グレンジャー因果関係分析に基づき、後部帯状回皮質とアルツハイマー病 (AD) の患者は、軽度認知障害 (MCI) と健常者と患者の脳全体の監督機能接続変更を調べた。

要約

障害機能接続デフォルト モード ネットワーク (DMN) ではアルツハイマー病 (AD) の進行に関与する可能性があります。後部帯状皮質 (PCC) は、AD の進行を監視するための潜在的なイメージング マーカーです。PCC と DMN、以外の地域のノードとの間の機能的結合の以前の研究に焦点をしなかったが、我々 の研究はこれらの見落とさの機能の接続を探索する努力。データを収集する機能磁気共鳴イメージ投射 (fMRI) とグレンジャー因果関係分析 (GCA) を使用しました。fMRI は、異なる脳部位間の動的相互作用を研究するための非侵襲的な方法を提供します。GCA は、ワンタイム シリーズは別の予測に有用かどうかを判断するための統計的仮説検定です。簡単に言えば、「既知の最後の瞬間、この時点での X の確率分布上のすべての情報」と「既知の Y、この時点での X の確率分布を除く最後の瞬間にすべての情報」を比較することによって判断される、Y と X と間の因果関係があるかどうかを決定します。この定義は、完全な情報源、および静止した時間的順序に基づいています。この分析の主要なステップは X を使用して回帰方程式を確立し、仮説のテストによって因果関係を描画する Y。GCA は、因果効果を測定することができます、ので機能的結合の異方性を調査し、PCC のハブ機能を探索に使用しました。ここでは、MRI スキャン、116 の参加者で仕切ったし、神経イメージング研究から得られたデータの前処理後、各ノードの因果関係を導き出す GCA を使用します。最後に、ダイレクト接続が脳全体に PCC と PCC に全脳から軽度認知障害 (MCI) と広告グループ間に有意な結論を得ました。

概要

広告は、病理組織学的、電気生理学、ニューロ イメージングの¹を使用して診断することができます中枢神経系の変性疾患です。メモリ関連 DMN は、広告に関連付けられている相互作用する脳の領域の重要なシステムとその機能異常広告^2,3の特徴であります。PCC は静止状態では伝統的な既定のネットワークの重要な地域をエピソード記憶、空間的注意、自己評価、その他認知機能^4,5,6,7で極めて重要な役割を果たしています。さらに、AD の進行を監視するためのイメージング マーカーしていることがあります。GCA を使用して、遼らは PCC が複数接続を持つ複数の習性の領域であり、脳機能構造⁸で重要な役割を果たしていることを発見しました。忠らは、PCC が DMN³内の他の領域のほとんどからの相互作用を受け収束の中心地であったことを報告しました。さらに、ミャオ族らは、DMN ハブ地域で PCC が他のノードの⁹と最大の因果関係を示した。一緒に、この証拠を示します、PCC のダイレクト接続は広告研究の貴重な PCC はさらにする必要がありますすべては DMN の重要な地域として詳細な検討。

前の研究は、PCC と DMN; 内その他の地域間の接続に限られていたしかし、その広告に及ぼす影響と同様に、DMN 外 PCC と脳領域間監督の機能的結合の変化まだされていない探検¹⁰。我々 の研究は、健常者、MCI 患者と AD 患者でこの未踏の機能的結合を詳しく調べる。全脳領域と PCC のダイレクト接続を観察することによって広告の進行に関連する脳の機能変化を明らかし、疾患の重症度を評価するため、新規の客観的な根拠を確立することを目指しました。

機能的結合は、脳の血液酸素レベル依存 (BOLD) fMRI 信号の同期の低周波ゆらぎ (LFFs) によって表すことができる地域間の相互作用を指します。したがって、PCC と他の脳部位間の機能的結合を観察するために、我々 は PCC 関心領域 (ROI) として用いた GCA、fMRI による PCC と脳全体のネットワークの間の機能的結合を分析しました。この手法は、直接神経イメージング研究¹¹から取得したデータを使用して各ノードの基本的な関係を派生します。最近では、GCA は、脳波と fMRI 研究脳領域¹²間の因果の影響を明らかにするために適用されています。すべてのこれらの研究では、GCA 技術が脳の各ノードの因果関係を検出するための最適なことがあります示されています。

プロトコル

This study was approved by the Ethics Committee of Zhejiang Provincial People's Hospital. Every enrolled subject signed a written informed consent.

1. Sample Classification and Screening

1. Diagnose and divide 116 patients into AD and MCI groups.
   NOTE: Use the 2011 National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) diagnostic criteria and the Mini-Mental State Examination (MMSE) criteria for identification and classification of MCI, which is described in detail in Yu et al.¹⁰
2. Select participants in the healthy control group.
   NOTE: The age, gender, and education level of the control group were matched with patients in the MCI and AD groups.
3. Assess all subjects by MMSE¹⁰.
4. Exclude the subjects who did not fulfill the inclusion criteria. For all subjects, the exclusion criteria are mentioned in Yu et al.¹⁰
5. Use MRI to scan subjects; exclude subjects with unusable data due to head movements.
   NOTE: Ultimately, we screened 26 patients with MCI, 32 patients with AD, and 58 controls.

2. Acquisition of Neuroimaging

1. Remove metal and magnetic objects before entering the 3.0 T MRI Laboratory.
2. Install an MRI receiving coil at the magnetic resonance scanning bed. The receiving coil is an 8 channel circularly polarized brain phased-array coil.
3. Instruct the participant to lie on the bed, and to remain supine with the head advanced and the long axis of the body along the long axis of the bed. Place the participant's head on the bracket of the coil, and make sure that the orbitomeatal line is perpendicular to bed.
4. Tell the participant to place the upper limbs to the sides of the body, keep eyes closed, not think of anything in particular, and move as little as possible during the scan. Place foam pads on the head in the bilateral temporal area to prevent head movement and provide headphones to reduce scanner noise for the participant.
   NOTE: Dimensions of the foam pad are: long diameter = 13 cm, short diameter = 10 cm, the thickest thickness = 7 cm, the thinnest thickness = 3 cm, average thickness = 5 cm.
5. Adjust the position of the head through the positioning light so that the sagittal positioning cursor is in the midline of the face, and the axis positioning cursor is parallel to the lateral canthus. Then move the bed to keep the axis positioning cursor on the eyebrows or 2 cm below it.
6. Move the head to the center of the magnet. Perform an fMRI brain scan, including gradient Echo-Planar Imaging (EPI-BOLD) and 3D-T1-MPRAGE imaging.
   NOTE: Use the following parameters:
   EPI-BOLD: TR/TE = 2,000/30 ms, layer thickness/layer pitch = 3.2/0.8 mm, 31 slices, matrix = 64 x 64, FOV = 220 x 220 mm, voxel size = 3 x 3 x 4 mm, flip angle = 90 °, scanning time of 484 s, and a total of 240 scanning images.
   3D-T1-MPRAGE imaging: TR/TE = 8.5/3.2 ms, flip angle = 15 °, field of view = 250 x 250 mm, matrix = 256 x 256, slice number = 176, slice thickness/gap = 1/0 mm, scanning time of 353 s, and a total of 192 scanning images.
7. Keep the patient safe when they are leaving the bed at the end of the scan.

3. Data Preprocessing

NOTE: Analyze the raw data for resting-state brain functions by using the Resting-State fMRI (rs-fMRI) Data Analysis Toolkit plus (RESTplus).

1. Open RESTplus through MATLAB and left click on Pipeline.
2. Import the relevant files into RESTplus. Select the work directory and then the starting EPI and T1 directories.
3. Convert DICOM files to NIFTI. Check off the DICOM to NIFTI box in preprocessing and check off the EPI DICOM to NIFTU and the T1 DICOM to NIFTI parameters.
4. Remove the first 10 time points by checking off the Remove first n time points and setting the n parameter as 10.
5. Set the slice timing according to rs-fMRI parameters. Check off the Slice timing box. Set the slice number according to the rs-fMRI parameters of the study. Enter the slice order.
   NOTE: The acquisition of data of each layer in the brain scan is not at the same time point, and thus, it needs to be calibrated to the same time point.
6. Correct the time and head motion. Check off Realign.
   NOTE: The exclusion criterion for excessive head motion was >2.0 mm translation or >2.0 ° rotation in any direction. In the RESTplus this is a default parameter (left click on the option of 'Realign').
7. Perform spatial normalization by using T1 image unified segmentation and all heads standardized to the same space. Check off Normalize and leave the default parameters at the bottom. Select the Normalize by using T1 image unified segmentation and European parameters.
   NOTE: Resample the rs-fMRI images with voxels of 3 × 3 × 3 mm, and other parameters in the RESTplus are default, just left click on the option of 'Normalize by using T1 image unified segmentation'.
8. Perform spatial smoothing using an isotropic Gaussian kernel with a full-width at half maximum (FWHM) of 6 mm. Check off Smooth.
9. Remove the linear trend by checking off Detrend.
10. Regress out signals from nuisance regressors (WM, CSF, Global) to increase signal-to-noise ratio. Check off Nuisance covariates regression and the following parameters: 6 head motion parameters, global mean signal, white matter signal, and cerebrospinal fluid signal.
    NOTE: During this step, set the 'Polynomial trend' as 1 as default, and choose the '6 head motion parameters', the 'Nuisance regressors (WM, CSF, Global)' and the 'add mean back' as default.
11. Use band-pass filtering to retain signals between 0.01 - 0.08 Hz. Remove high-frequency physiological noise, and low-frequency drift. Check off Filter.

4. Directed Connectivity Analysis

NOTE: Perform GCA combined with the BOLD signals for each voxel in the whole brain after extracting the average BOLD signal intensity in the seed area.

1. Perform the voxel-wise GCA by using the REST-GCA in the REST toolbox. In the post-processing box, check off GCA.
2. Set the 'order' as 1 as default. Select the parameters in the input.
3. Define ROI and identify seed points of interest in the PCC. Select Define ROI and choose the Spherical ROI. Select Next. Set the center coordinates and radius of the seed ROI based on the known data and select OK.
   NOTE: An ROI for the DMN was placed at the PCC (centering at x = 0, y = -53, z = 26 with radius = 6 mm), as in a previous study¹³.
4. Select Run and OK to run the program.
5. Find folders named ZGCA and GCA after processing of relevant file data. Sort out the files of ZGCA and classify them into four subfolders, xx, xy, yx, yy accordingly.
   NOTE: Later, mainly use the xy and yx subfolders. The three sets of file data ('AD' 'MCI' 'NC') are all processed and sorted according to steps 3.1 - 4.5 above.
6. Open RESTplus through MATLAB and left click on Statistical Analysis. Left click on REST Two-Sample T-Test.
7. Name the output result as T1xy and set the output directory. Left click on Add Group Images to open the xy subfolder in the AD Results folder and the xy subfolder in the NC Results folder.
8. In the option of Mask File, left click to open the BrainMask_05_61*73*61 subfile in the 'mask' folder.
9. Select Compute to run the program.
10. Name the output result as T2xy and set the output directory. Left click on Add Group Images to open the xy subfolder in the AD Results folder and the xy subfolder in the MCI Results folder. Repeat steps 4.8 - 4.9.
11. Name the output result as T3xy and set the output directory. Left click on Add Group Images to open the xy subfolder in the MCI Results folder and the xy subfolder in the NC Results folder. Repeat steps 4.8 - 4.9.
12. Name the output result as T1yx and set the output directory. Left click on Add Group Images to open the yx subfolder in the AD Results folder and the yx subfolder in the NC Results folder. Repeat steps 4.8 - 4.9.
13. Name the output result as T2yx and set the output directory. Left click on Add Group Images to open the yx subfolder in the AD Results folder and the yx subfolder in the MCI Results folder. Repeat steps 4.8 - 4.9.
14. Name the output result as T3yx and set the output directory. Left click on Add Group Images to open the yx subfolder in the MCI Results folder and the yx subfolder in the NC Results folder. Repeat steps 4.8 - 4.9.
15. Finally, obtain the six result files by following steps 4.6 - 4.14 and left click on viewer of RESTplus to view the result. Import the template named ch2 in Underlay.
16. Find the six result files in the output directory and fill in the Overlay one by one. Obtain the final result graph, and the six result files that correspond to the six graphs.
17. Use Statistical Product and Service Solutions (SPSS) to process the data obtained from the previous step.
    1. Present Continuous variables as means and Standard Deviations (SD).
    2. Present categorical variables as numbers and percentages, then use the chi-square test.
       NOTE: All p-values of <0.05 were considered statistically significant.

結果

Demographic information

Table 1 presents the characteristics of the subjects. All the subjects had an education level of junior school or above. Age, gender, and education level were similar between the three groups (P >0.05), while the MMSE scores were significantly different (p <0.01).

Directed brain functional connectivity

ディスカッション

監督機能への接続、PCC から全脳および脳全体から PCC 広告の間の比較のためのプロセスについて述べる MCI とコントロールのグループ。さらに、このプロセスの重要なステップは、分類と実験の前に、サンプルのスクリーニングです。したがって、分類および審査基準は、間違いである場合、結果の精度に影響するので重要に。私たち使用の識別と MCI の分類基準と MMSE、2011 実践 ADRDA 診断基?...

資料

Name	Company	Catalog Number	Comments
116 patients	Zhejiang Provincial People’s hospital	-	This study was approved by the ethics committee of Zhejiang Provincial People’s hospital. Every enrolled subject signed a written informed consent form.
Siemens Trio 3.0 T MRI scanner	Siemens, Erlangen, Germany	20571	Equipped with AudioComfort that reduces acoustic noise up to 90%; Provides high performance at a low noise level; Ultra light-weight coil; Unique MRI sequence design; Supports up to 400 pounds without restrictions.
RESTplus	Hangzhou Normal University, Hangzhou, Zhejiang, China	20160122	RESTplus evolved from REST (Resting-State fMRI Data Analysis Toolkit), a convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity(ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality, degree centrality, voxel-mirrored homotopic connectivity (VMHC) and perform statistical analysis.
DPARSF	Hangzhou Normal University, Hangzhou, Zhejiang, China	130615	Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software within DPABI, which is based on SPM. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed data, functional connectivity, ReHo, ALFF/fALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results.
SPSS	SPSS Inc., Chicago, IL, USA	-	SPSS offers detailed analysis options to look deeper into your data and spot trends that you might not have noticed.